176 lines
5.8 KiB
Python
176 lines
5.8 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
DataFrame,
|
|
IntervalIndex,
|
|
Series,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestIntervalIndex:
|
|
@pytest.fixture
|
|
def series_with_interval_index(self):
|
|
return Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6)))
|
|
|
|
def test_getitem_with_scalar(self, series_with_interval_index, indexer_sl):
|
|
|
|
ser = series_with_interval_index.copy()
|
|
|
|
expected = ser.iloc[:3]
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[:3])
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[:2.5])
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[0.1:2.5])
|
|
if indexer_sl is tm.loc:
|
|
tm.assert_series_equal(expected, ser.loc[-1:3])
|
|
|
|
expected = ser.iloc[1:4]
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 2.5, 3.5]])
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[[2, 3, 4]])
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 3, 4]])
|
|
|
|
expected = ser.iloc[2:5]
|
|
tm.assert_series_equal(expected, indexer_sl(ser)[ser >= 2])
|
|
|
|
@pytest.mark.parametrize("direction", ["increasing", "decreasing"])
|
|
def test_getitem_nonoverlapping_monotonic(self, direction, closed, indexer_sl):
|
|
tpls = [(0, 1), (2, 3), (4, 5)]
|
|
if direction == "decreasing":
|
|
tpls = tpls[::-1]
|
|
|
|
idx = IntervalIndex.from_tuples(tpls, closed=closed)
|
|
ser = Series(list("abc"), idx)
|
|
|
|
for key, expected in zip(idx.left, ser):
|
|
if idx.closed_left:
|
|
assert indexer_sl(ser)[key] == expected
|
|
else:
|
|
with pytest.raises(KeyError, match=str(key)):
|
|
indexer_sl(ser)[key]
|
|
|
|
for key, expected in zip(idx.right, ser):
|
|
if idx.closed_right:
|
|
assert indexer_sl(ser)[key] == expected
|
|
else:
|
|
with pytest.raises(KeyError, match=str(key)):
|
|
indexer_sl(ser)[key]
|
|
|
|
for key, expected in zip(idx.mid, ser):
|
|
assert indexer_sl(ser)[key] == expected
|
|
|
|
def test_getitem_non_matching(self, series_with_interval_index, indexer_sl):
|
|
ser = series_with_interval_index.copy()
|
|
|
|
# this is a departure from our current
|
|
# indexing scheme, but simpler
|
|
with pytest.raises(KeyError, match=r"\[-1\] not in index"):
|
|
indexer_sl(ser)[[-1, 3, 4, 5]]
|
|
|
|
with pytest.raises(KeyError, match=r"\[-1\] not in index"):
|
|
indexer_sl(ser)[[-1, 3]]
|
|
|
|
@pytest.mark.slow
|
|
def test_loc_getitem_large_series(self):
|
|
ser = Series(
|
|
np.arange(1000000), index=IntervalIndex.from_breaks(np.arange(1000001))
|
|
)
|
|
|
|
result1 = ser.loc[:80000]
|
|
result2 = ser.loc[0:80000]
|
|
result3 = ser.loc[0:80000:1]
|
|
tm.assert_series_equal(result1, result2)
|
|
tm.assert_series_equal(result1, result3)
|
|
|
|
def test_loc_getitem_frame(self):
|
|
# CategoricalIndex with IntervalIndex categories
|
|
df = DataFrame({"A": range(10)})
|
|
ser = pd.cut(df.A, 5)
|
|
df["B"] = ser
|
|
df = df.set_index("B")
|
|
|
|
result = df.loc[4]
|
|
expected = df.iloc[4:6]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
with pytest.raises(KeyError, match="10"):
|
|
df.loc[10]
|
|
|
|
# single list-like
|
|
result = df.loc[[4]]
|
|
expected = df.iloc[4:6]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# non-unique
|
|
result = df.loc[[4, 5]]
|
|
expected = df.take([4, 5, 4, 5])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
with pytest.raises(KeyError, match=r"None of \[\[10\]\] are"):
|
|
df.loc[[10]]
|
|
|
|
# partial missing
|
|
with pytest.raises(KeyError, match=r"\[10\] not in index"):
|
|
df.loc[[10, 4]]
|
|
|
|
def test_getitem_interval_with_nans(self, frame_or_series, indexer_sl):
|
|
# GH#41831
|
|
|
|
index = IntervalIndex([np.nan, np.nan])
|
|
key = index[:-1]
|
|
|
|
obj = frame_or_series(range(2), index=index)
|
|
if frame_or_series is DataFrame and indexer_sl is tm.setitem:
|
|
obj = obj.T
|
|
|
|
result = indexer_sl(obj)[key]
|
|
expected = obj
|
|
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
class TestIntervalIndexInsideMultiIndex:
|
|
def test_mi_intervalindex_slicing_with_scalar(self):
|
|
# GH#27456
|
|
ii = IntervalIndex.from_arrays(
|
|
[0, 1, 10, 11, 0, 1, 10, 11], [1, 2, 11, 12, 1, 2, 11, 12], name="MP"
|
|
)
|
|
idx = pd.MultiIndex.from_arrays(
|
|
[
|
|
pd.Index(["FC", "FC", "FC", "FC", "OWNER", "OWNER", "OWNER", "OWNER"]),
|
|
pd.Index(
|
|
["RID1", "RID1", "RID2", "RID2", "RID1", "RID1", "RID2", "RID2"]
|
|
),
|
|
ii,
|
|
]
|
|
)
|
|
|
|
idx.names = ["Item", "RID", "MP"]
|
|
df = DataFrame({"value": [1, 2, 3, 4, 5, 6, 7, 8]})
|
|
df.index = idx
|
|
|
|
query_df = DataFrame(
|
|
{
|
|
"Item": ["FC", "OWNER", "FC", "OWNER", "OWNER"],
|
|
"RID": ["RID1", "RID1", "RID1", "RID2", "RID2"],
|
|
"MP": [0.2, 1.5, 1.6, 11.1, 10.9],
|
|
}
|
|
)
|
|
|
|
query_df = query_df.sort_index()
|
|
|
|
idx = pd.MultiIndex.from_arrays([query_df.Item, query_df.RID, query_df.MP])
|
|
query_df.index = idx
|
|
result = df.value.loc[query_df.index]
|
|
|
|
# the IntervalIndex level is indexed with floats, which map to
|
|
# the intervals containing them. Matching the behavior we would get
|
|
# with _only_ an IntervalIndex, we get an IntervalIndex level back.
|
|
sliced_level = ii.take([0, 1, 1, 3, 2])
|
|
expected_index = pd.MultiIndex.from_arrays(
|
|
[idx.get_level_values(0), idx.get_level_values(1), sliced_level]
|
|
)
|
|
expected = Series([1, 6, 2, 8, 7], index=expected_index, name="value")
|
|
tm.assert_series_equal(result, expected)
|