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2985 lines
101 KiB
2985 lines
101 KiB
""" test label based indexing with loc """
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from collections import namedtuple
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from datetime import (
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date,
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datetime,
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time,
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timedelta,
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)
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import re
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from dateutil.tz import gettz
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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Categorical,
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CategoricalIndex,
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DataFrame,
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DatetimeIndex,
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Index,
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IndexSlice,
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MultiIndex,
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Period,
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Series,
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SparseDtype,
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Timedelta,
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Timestamp,
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date_range,
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timedelta_range,
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to_datetime,
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to_timedelta,
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)
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import pandas._testing as tm
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from pandas.api.types import is_scalar
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from pandas.core.api import Float64Index
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from pandas.core.indexing import (
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IndexingError,
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_one_ellipsis_message,
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)
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from pandas.tests.indexing.common import Base
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class TestLoc(Base):
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def test_loc_getitem_int(self):
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# int label
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self.check_result("loc", 2, typs=["labels"], fails=KeyError)
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def test_loc_getitem_label(self):
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# label
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self.check_result("loc", "c", typs=["empty"], fails=KeyError)
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def test_loc_getitem_label_out_of_range(self):
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# out of range label
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self.check_result(
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"loc", "f", typs=["ints", "uints", "labels", "mixed", "ts"], fails=KeyError
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)
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self.check_result("loc", "f", typs=["floats"], fails=KeyError)
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self.check_result("loc", "f", typs=["floats"], fails=KeyError)
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self.check_result("loc", 20, typs=["ints", "uints", "mixed"], fails=KeyError)
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self.check_result("loc", 20, typs=["labels"], fails=KeyError)
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self.check_result("loc", 20, typs=["ts"], axes=0, fails=KeyError)
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self.check_result("loc", 20, typs=["floats"], axes=0, fails=KeyError)
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def test_loc_getitem_label_list(self):
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# list of labels
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self.check_result(
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"loc", [0, 1, 2], typs=["ints", "uints", "floats"], fails=KeyError
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)
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self.check_result(
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"loc", [1, 3.0, "A"], typs=["ints", "uints", "floats"], fails=KeyError
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)
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def test_loc_getitem_label_list_with_missing(self):
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self.check_result("loc", [0, 1, 2], typs=["empty"], fails=KeyError)
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self.check_result(
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"loc", [0, 2, 10], typs=["ints", "uints", "floats"], axes=0, fails=KeyError
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)
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self.check_result(
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"loc", [3, 6, 7], typs=["ints", "uints", "floats"], axes=1, fails=KeyError
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)
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# GH 17758 - MultiIndex and missing keys
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self.check_result(
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"loc", [(1, 3), (1, 4), (2, 5)], typs=["multi"], axes=0, fails=KeyError
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)
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def test_loc_getitem_label_list_fails(self):
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# fails
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self.check_result(
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"loc", [20, 30, 40], typs=["ints", "uints"], axes=1, fails=KeyError
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)
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def test_loc_getitem_label_array_like(self):
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# TODO: test something?
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# array like
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pass
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def test_loc_getitem_bool(self):
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# boolean indexers
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b = [True, False, True, False]
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self.check_result("loc", b, typs=["empty"], fails=IndexError)
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def test_loc_getitem_label_slice(self):
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# label slices (with ints)
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# real label slices
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# GH 14316
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self.check_result(
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"loc",
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slice(1, 3),
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typs=["labels", "mixed", "empty", "ts", "floats"],
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fails=TypeError,
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)
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self.check_result(
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"loc", slice("20130102", "20130104"), typs=["ts"], axes=1, fails=TypeError
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)
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self.check_result("loc", slice(2, 8), typs=["mixed"], axes=0, fails=TypeError)
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self.check_result("loc", slice(2, 8), typs=["mixed"], axes=1, fails=KeyError)
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self.check_result(
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"loc", slice(2, 4, 2), typs=["mixed"], axes=0, fails=TypeError
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)
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def test_setitem_from_duplicate_axis(self):
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# GH#34034
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df = DataFrame(
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[[20, "a"], [200, "a"], [200, "a"]],
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columns=["col1", "col2"],
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index=[10, 1, 1],
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)
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df.loc[1, "col1"] = np.arange(2)
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expected = DataFrame(
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[[20, "a"], [0, "a"], [1, "a"]], columns=["col1", "col2"], index=[10, 1, 1]
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)
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tm.assert_frame_equal(df, expected)
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def test_column_types_consistent(self):
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# GH 26779
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df = DataFrame(
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data={
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"channel": [1, 2, 3],
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"A": ["String 1", np.NaN, "String 2"],
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"B": [
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Timestamp("2019-06-11 11:00:00"),
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pd.NaT,
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Timestamp("2019-06-11 12:00:00"),
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],
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}
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)
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df2 = DataFrame(
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data={"A": ["String 3"], "B": [Timestamp("2019-06-11 12:00:00")]}
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)
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# Change Columns A and B to df2.values wherever Column A is NaN
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df.loc[df["A"].isna(), ["A", "B"]] = df2.values
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expected = DataFrame(
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data={
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"channel": [1, 2, 3],
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"A": ["String 1", "String 3", "String 2"],
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"B": [
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Timestamp("2019-06-11 11:00:00"),
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Timestamp("2019-06-11 12:00:00"),
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Timestamp("2019-06-11 12:00:00"),
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],
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}
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)
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize(
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"obj, key, exp",
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[
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(
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DataFrame([[1]], columns=Index([False])),
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IndexSlice[:, False],
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Series([1], name=False),
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),
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(Series([1], index=Index([False])), False, [1]),
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(DataFrame([[1]], index=Index([False])), False, Series([1], name=False)),
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],
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)
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def test_loc_getitem_single_boolean_arg(self, obj, key, exp):
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# GH 44322
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res = obj.loc[key]
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if isinstance(exp, (DataFrame, Series)):
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tm.assert_equal(res, exp)
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else:
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assert res == exp
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class TestLocBaseIndependent:
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# Tests for loc that do not depend on subclassing Base
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@pytest.mark.parametrize(
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"msg, key",
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[
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(r"Period\('2019', 'A-DEC'\), 'foo', 'bar'", (Period(2019), "foo", "bar")),
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(r"Period\('2019', 'A-DEC'\), 'y1', 'bar'", (Period(2019), "y1", "bar")),
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(r"Period\('2019', 'A-DEC'\), 'foo', 'z1'", (Period(2019), "foo", "z1")),
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(
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r"Period\('2018', 'A-DEC'\), Period\('2016', 'A-DEC'\), 'bar'",
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(Period(2018), Period(2016), "bar"),
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),
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(r"Period\('2018', 'A-DEC'\), 'foo', 'y1'", (Period(2018), "foo", "y1")),
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(
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r"Period\('2017', 'A-DEC'\), 'foo', Period\('2015', 'A-DEC'\)",
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(Period(2017), "foo", Period(2015)),
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),
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(r"Period\('2017', 'A-DEC'\), 'z1', 'bar'", (Period(2017), "z1", "bar")),
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],
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)
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def test_contains_raise_error_if_period_index_is_in_multi_index(self, msg, key):
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# GH#20684
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"""
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parse_time_string return parameter if type not matched.
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PeriodIndex.get_loc takes returned value from parse_time_string as a tuple.
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If first argument is Period and a tuple has 3 items,
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process go on not raise exception
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"""
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df = DataFrame(
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{
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"A": [Period(2019), "x1", "x2"],
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"B": [Period(2018), Period(2016), "y1"],
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"C": [Period(2017), "z1", Period(2015)],
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"V1": [1, 2, 3],
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"V2": [10, 20, 30],
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}
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).set_index(["A", "B", "C"])
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with pytest.raises(KeyError, match=msg):
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df.loc[key]
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def test_loc_getitem_missing_unicode_key(self):
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df = DataFrame({"a": [1]})
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with pytest.raises(KeyError, match="\u05d0"):
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df.loc[:, "\u05d0"] # should not raise UnicodeEncodeError
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def test_loc_getitem_dups(self):
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# GH 5678
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# repeated getitems on a dup index returning a ndarray
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df = DataFrame(
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np.random.random_sample((20, 5)), index=["ABCDE"[x % 5] for x in range(20)]
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)
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expected = df.loc["A", 0]
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result = df.loc[:, 0].loc["A"]
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tm.assert_series_equal(result, expected)
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def test_loc_getitem_dups2(self):
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# GH4726
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# dup indexing with iloc/loc
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df = DataFrame(
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[[1, 2, "foo", "bar", Timestamp("20130101")]],
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columns=["a", "a", "a", "a", "a"],
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index=[1],
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)
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expected = Series(
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[1, 2, "foo", "bar", Timestamp("20130101")],
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index=["a", "a", "a", "a", "a"],
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name=1,
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)
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result = df.iloc[0]
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tm.assert_series_equal(result, expected)
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result = df.loc[1]
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tm.assert_series_equal(result, expected)
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def test_loc_setitem_dups(self):
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# GH 6541
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df_orig = DataFrame(
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{
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"me": list("rttti"),
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"foo": list("aaade"),
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"bar": np.arange(5, dtype="float64") * 1.34 + 2,
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"bar2": np.arange(5, dtype="float64") * -0.34 + 2,
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}
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).set_index("me")
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indexer = (
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"r",
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["bar", "bar2"],
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)
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df = df_orig.copy()
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df.loc[indexer] *= 2.0
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tm.assert_series_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer])
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indexer = (
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"r",
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"bar",
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)
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df = df_orig.copy()
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df.loc[indexer] *= 2.0
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assert df.loc[indexer] == 2.0 * df_orig.loc[indexer]
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indexer = (
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"t",
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["bar", "bar2"],
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)
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df = df_orig.copy()
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df.loc[indexer] *= 2.0
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tm.assert_frame_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer])
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def test_loc_setitem_slice(self):
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# GH10503
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# assigning the same type should not change the type
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df1 = DataFrame({"a": [0, 1, 1], "b": Series([100, 200, 300], dtype="uint32")})
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ix = df1["a"] == 1
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newb1 = df1.loc[ix, "b"] + 1
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df1.loc[ix, "b"] = newb1
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expected = DataFrame(
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{"a": [0, 1, 1], "b": Series([100, 201, 301], dtype="uint32")}
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)
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tm.assert_frame_equal(df1, expected)
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# assigning a new type should get the inferred type
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df2 = DataFrame({"a": [0, 1, 1], "b": [100, 200, 300]}, dtype="uint64")
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ix = df1["a"] == 1
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newb2 = df2.loc[ix, "b"]
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df1.loc[ix, "b"] = newb2
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expected = DataFrame({"a": [0, 1, 1], "b": [100, 200, 300]}, dtype="uint64")
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tm.assert_frame_equal(df2, expected)
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def test_loc_setitem_dtype(self):
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# GH31340
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df = DataFrame({"id": ["A"], "a": [1.2], "b": [0.0], "c": [-2.5]})
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cols = ["a", "b", "c"]
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df.loc[:, cols] = df.loc[:, cols].astype("float32")
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expected = DataFrame(
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{
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"id": ["A"],
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"a": np.array([1.2], dtype="float32"),
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"b": np.array([0.0], dtype="float32"),
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"c": np.array([-2.5], dtype="float32"),
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}
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) # id is inferred as object
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tm.assert_frame_equal(df, expected)
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def test_getitem_label_list_with_missing(self):
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s = Series(range(3), index=["a", "b", "c"])
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# consistency
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with pytest.raises(KeyError, match="not in index"):
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s[["a", "d"]]
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s = Series(range(3))
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with pytest.raises(KeyError, match="not in index"):
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s[[0, 3]]
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@pytest.mark.parametrize("index", [[True, False], [True, False, True, False]])
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def test_loc_getitem_bool_diff_len(self, index):
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# GH26658
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s = Series([1, 2, 3])
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msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}"
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with pytest.raises(IndexError, match=msg):
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s.loc[index]
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def test_loc_getitem_int_slice(self):
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# TODO: test something here?
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pass
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def test_loc_to_fail(self):
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# GH3449
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df = DataFrame(
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np.random.random((3, 3)), index=["a", "b", "c"], columns=["e", "f", "g"]
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)
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# raise a KeyError?
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msg = (
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r"\"None of \[Int64Index\(\[1, 2\], dtype='int64'\)\] are "
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r"in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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df.loc[[1, 2], [1, 2]]
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def test_loc_to_fail2(self):
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# GH 7496
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# loc should not fallback
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s = Series(dtype=object)
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s.loc[1] = 1
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s.loc["a"] = 2
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with pytest.raises(KeyError, match=r"^-1$"):
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s.loc[-1]
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msg = (
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r"\"None of \[Int64Index\(\[-1, -2\], dtype='int64'\)\] are "
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r"in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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s.loc[[-1, -2]]
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msg = r"\"None of \[Index\(\['4'\], dtype='object'\)\] are in the \[index\]\""
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with pytest.raises(KeyError, match=msg):
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s.loc[["4"]]
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s.loc[-1] = 3
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with pytest.raises(KeyError, match="not in index"):
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s.loc[[-1, -2]]
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s["a"] = 2
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msg = (
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r"\"None of \[Int64Index\(\[-2\], dtype='int64'\)\] are "
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r"in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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s.loc[[-2]]
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del s["a"]
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with pytest.raises(KeyError, match=msg):
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s.loc[[-2]] = 0
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|
def test_loc_to_fail3(self):
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# inconsistency between .loc[values] and .loc[values,:]
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# GH 7999
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df = DataFrame([["a"], ["b"]], index=[1, 2], columns=["value"])
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|
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|
msg = (
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r"\"None of \[Int64Index\(\[3\], dtype='int64'\)\] are "
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r"in the \[index\]\""
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)
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with pytest.raises(KeyError, match=msg):
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df.loc[[3], :]
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|
with pytest.raises(KeyError, match=msg):
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df.loc[[3]]
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|
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|
def test_loc_getitem_list_with_fail(self):
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# 15747
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|
# should KeyError if *any* missing labels
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|
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|
s = Series([1, 2, 3])
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|
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|
s.loc[[2]]
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|
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|
with pytest.raises(
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KeyError,
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|
match=re.escape(
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"\"None of [Int64Index([3], dtype='int64')] are in the [index]\""
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),
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|
):
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s.loc[[3]]
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|
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|
# a non-match and a match
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|
with pytest.raises(KeyError, match="not in index"):
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s.loc[[2, 3]]
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|
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|
def test_loc_index(self):
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# gh-17131
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|
# a boolean index should index like a boolean numpy array
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|
df = DataFrame(
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np.random.random(size=(5, 10)),
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index=["alpha_0", "alpha_1", "alpha_2", "beta_0", "beta_1"],
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)
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mask = df.index.map(lambda x: "alpha" in x)
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expected = df.loc[np.array(mask)]
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result = df.loc[mask]
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tm.assert_frame_equal(result, expected)
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result = df.loc[mask.values]
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tm.assert_frame_equal(result, expected)
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result = df.loc[pd.array(mask, dtype="boolean")]
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tm.assert_frame_equal(result, expected)
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|
def test_loc_general(self):
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df = DataFrame(
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np.random.rand(4, 4),
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columns=["A", "B", "C", "D"],
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index=["A", "B", "C", "D"],
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)
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# want this to work
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result = df.loc[:, "A":"B"].iloc[0:2, :]
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assert (result.columns == ["A", "B"]).all()
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assert (result.index == ["A", "B"]).all()
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# mixed type
|
|
result = DataFrame({"a": [Timestamp("20130101")], "b": [1]}).iloc[0]
|
|
expected = Series([Timestamp("20130101"), 1], index=["a", "b"], name=0)
|
|
tm.assert_series_equal(result, expected)
|
|
assert result.dtype == object
|
|
|
|
@pytest.fixture
|
|
def frame_for_consistency(self):
|
|
return DataFrame(
|
|
{
|
|
"date": date_range("2000-01-01", "2000-01-5"),
|
|
"val": Series(range(5), dtype=np.int64),
|
|
}
|
|
)
|
|
|
|
@pytest.mark.parametrize(
|
|
"val",
|
|
[0, np.array(0, dtype=np.int64), np.array([0, 0, 0, 0, 0], dtype=np.int64)],
|
|
)
|
|
def test_loc_setitem_consistency(self, frame_for_consistency, val):
|
|
# GH 6149
|
|
# coerce similarly for setitem and loc when rows have a null-slice
|
|
expected = DataFrame(
|
|
{
|
|
"date": Series(0, index=range(5), dtype=np.int64),
|
|
"val": Series(range(5), dtype=np.int64),
|
|
}
|
|
)
|
|
df = frame_for_consistency.copy()
|
|
df.loc[:, "date"] = val
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_consistency_dt64_to_str(self, frame_for_consistency):
|
|
# GH 6149
|
|
# coerce similarly for setitem and loc when rows have a null-slice
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"date": Series("foo", index=range(5)),
|
|
"val": Series(range(5), dtype=np.int64),
|
|
}
|
|
)
|
|
df = frame_for_consistency.copy()
|
|
df.loc[:, "date"] = "foo"
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_consistency_dt64_to_float(self, frame_for_consistency):
|
|
# GH 6149
|
|
# coerce similarly for setitem and loc when rows have a null-slice
|
|
expected = DataFrame(
|
|
{
|
|
"date": Series(1.0, index=range(5)),
|
|
"val": Series(range(5), dtype=np.int64),
|
|
}
|
|
)
|
|
df = frame_for_consistency.copy()
|
|
df.loc[:, "date"] = 1.0
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_consistency_single_row(self):
|
|
# GH 15494
|
|
# setting on frame with single row
|
|
df = DataFrame({"date": Series([Timestamp("20180101")])})
|
|
df.loc[:, "date"] = "string"
|
|
expected = DataFrame({"date": Series(["string"])})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_consistency_empty(self):
|
|
# empty (essentially noops)
|
|
expected = DataFrame(columns=["x", "y"])
|
|
expected["x"] = expected["x"].astype(np.int64)
|
|
df = DataFrame(columns=["x", "y"])
|
|
df.loc[:, "x"] = 1
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
df = DataFrame(columns=["x", "y"])
|
|
df["x"] = 1
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_consistency_slice_column_len(self):
|
|
# .loc[:,column] setting with slice == len of the column
|
|
# GH10408
|
|
levels = [
|
|
["Region_1"] * 4,
|
|
["Site_1", "Site_1", "Site_2", "Site_2"],
|
|
[3987227376, 3980680971, 3977723249, 3977723089],
|
|
]
|
|
mi = MultiIndex.from_arrays(levels, names=["Region", "Site", "RespondentID"])
|
|
|
|
clevels = [
|
|
["Respondent", "Respondent", "Respondent", "OtherCat", "OtherCat"],
|
|
["Something", "StartDate", "EndDate", "Yes/No", "SomethingElse"],
|
|
]
|
|
cols = MultiIndex.from_arrays(clevels, names=["Level_0", "Level_1"])
|
|
|
|
values = [
|
|
["A", "5/25/2015 10:59", "5/25/2015 11:22", "Yes", np.nan],
|
|
["A", "5/21/2015 9:40", "5/21/2015 9:52", "Yes", "Yes"],
|
|
["A", "5/20/2015 8:27", "5/20/2015 8:41", "Yes", np.nan],
|
|
["A", "5/20/2015 8:33", "5/20/2015 9:09", "Yes", "No"],
|
|
]
|
|
df = DataFrame(values, index=mi, columns=cols)
|
|
|
|
df.loc[:, ("Respondent", "StartDate")] = to_datetime(
|
|
df.loc[:, ("Respondent", "StartDate")]
|
|
)
|
|
df.loc[:, ("Respondent", "EndDate")] = to_datetime(
|
|
df.loc[:, ("Respondent", "EndDate")]
|
|
)
|
|
df.loc[:, ("Respondent", "Duration")] = (
|
|
df.loc[:, ("Respondent", "EndDate")]
|
|
- df.loc[:, ("Respondent", "StartDate")]
|
|
)
|
|
|
|
df.loc[:, ("Respondent", "Duration")] = df.loc[
|
|
:, ("Respondent", "Duration")
|
|
].astype("timedelta64[s]")
|
|
expected = Series(
|
|
[1380, 720, 840, 2160.0], index=df.index, name=("Respondent", "Duration")
|
|
)
|
|
tm.assert_series_equal(df[("Respondent", "Duration")], expected)
|
|
|
|
@pytest.mark.parametrize("unit", ["Y", "M", "D", "h", "m", "s", "ms", "us"])
|
|
def test_loc_assign_non_ns_datetime(self, unit):
|
|
# GH 27395, non-ns dtype assignment via .loc should work
|
|
# and return the same result when using simple assignment
|
|
df = DataFrame(
|
|
{
|
|
"timestamp": [
|
|
np.datetime64("2017-02-11 12:41:29"),
|
|
np.datetime64("1991-11-07 04:22:37"),
|
|
]
|
|
}
|
|
)
|
|
|
|
df.loc[:, unit] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]")
|
|
df["expected"] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]")
|
|
expected = Series(df.loc[:, "expected"], name=unit)
|
|
tm.assert_series_equal(df.loc[:, unit], expected)
|
|
|
|
def test_loc_modify_datetime(self):
|
|
# see gh-28837
|
|
df = DataFrame.from_dict(
|
|
{"date": [1485264372711, 1485265925110, 1540215845888, 1540282121025]}
|
|
)
|
|
|
|
df["date_dt"] = to_datetime(df["date"], unit="ms", cache=True)
|
|
|
|
df.loc[:, "date_dt_cp"] = df.loc[:, "date_dt"]
|
|
df.loc[[2, 3], "date_dt_cp"] = df.loc[[2, 3], "date_dt"]
|
|
|
|
expected = DataFrame(
|
|
[
|
|
[1485264372711, "2017-01-24 13:26:12.711", "2017-01-24 13:26:12.711"],
|
|
[1485265925110, "2017-01-24 13:52:05.110", "2017-01-24 13:52:05.110"],
|
|
[1540215845888, "2018-10-22 13:44:05.888", "2018-10-22 13:44:05.888"],
|
|
[1540282121025, "2018-10-23 08:08:41.025", "2018-10-23 08:08:41.025"],
|
|
],
|
|
columns=["date", "date_dt", "date_dt_cp"],
|
|
)
|
|
|
|
columns = ["date_dt", "date_dt_cp"]
|
|
expected[columns] = expected[columns].apply(to_datetime)
|
|
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame_with_reindex(self, using_array_manager):
|
|
# GH#6254 setting issue
|
|
df = DataFrame(index=[3, 5, 4], columns=["A"], dtype=float)
|
|
df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64")
|
|
|
|
# setting integer values into a float dataframe with loc is inplace,
|
|
# so we retain float dtype
|
|
ser = Series([2, 3, 1], index=[3, 5, 4], dtype=float)
|
|
if using_array_manager:
|
|
# TODO(ArrayManager) with "split" path, we still overwrite the column
|
|
# and therefore don't take the dtype of the underlying object into account
|
|
ser = Series([2, 3, 1], index=[3, 5, 4], dtype="int64")
|
|
expected = DataFrame({"A": ser})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame_with_reindex_mixed(self):
|
|
# GH#40480
|
|
df = DataFrame(index=[3, 5, 4], columns=["A", "B"], dtype=float)
|
|
df["B"] = "string"
|
|
df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64")
|
|
ser = Series([2, 3, 1], index=[3, 5, 4], dtype="int64")
|
|
expected = DataFrame({"A": ser})
|
|
expected["B"] = "string"
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame_with_inverted_slice(self):
|
|
# GH#40480
|
|
df = DataFrame(index=[1, 2, 3], columns=["A", "B"], dtype=float)
|
|
df["B"] = "string"
|
|
df.loc[slice(3, 0, -1), "A"] = np.array([1, 2, 3], dtype="int64")
|
|
expected = DataFrame({"A": [3, 2, 1], "B": "string"}, index=[1, 2, 3])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# TODO(ArrayManager) "split" path overwrites column and therefore don't take
|
|
# the dtype of the underlying object into account
|
|
@td.skip_array_manager_not_yet_implemented
|
|
def test_loc_setitem_empty_frame(self):
|
|
# GH#6252 setting with an empty frame
|
|
keys1 = ["@" + str(i) for i in range(5)]
|
|
val1 = np.arange(5, dtype="int64")
|
|
|
|
keys2 = ["@" + str(i) for i in range(4)]
|
|
val2 = np.arange(4, dtype="int64")
|
|
|
|
index = list(set(keys1).union(keys2))
|
|
df = DataFrame(index=index)
|
|
df["A"] = np.nan
|
|
df.loc[keys1, "A"] = val1
|
|
|
|
df["B"] = np.nan
|
|
df.loc[keys2, "B"] = val2
|
|
|
|
# Because df["A"] was initialized as float64, setting values into it
|
|
# is inplace, so that dtype is retained
|
|
sera = Series(val1, index=keys1, dtype=np.float64)
|
|
serb = Series(val2, index=keys2)
|
|
expected = DataFrame({"A": sera, "B": serb}).reindex(index=index)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame(self):
|
|
df = DataFrame(np.random.randn(4, 4), index=list("abcd"), columns=list("ABCD"))
|
|
|
|
result = df.iloc[0, 0]
|
|
|
|
df.loc["a", "A"] = 1
|
|
result = df.loc["a", "A"]
|
|
assert result == 1
|
|
|
|
result = df.iloc[0, 0]
|
|
assert result == 1
|
|
|
|
df.loc[:, "B":"D"] = 0
|
|
expected = df.loc[:, "B":"D"]
|
|
result = df.iloc[:, 1:]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_setitem_frame_nan_int_coercion_invalid(self):
|
|
# GH 8669
|
|
# invalid coercion of nan -> int
|
|
df = DataFrame({"A": [1, 2, 3], "B": np.nan})
|
|
df.loc[df.B > df.A, "B"] = df.A
|
|
expected = DataFrame({"A": [1, 2, 3], "B": np.nan})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame_mixed_labels(self):
|
|
# GH 6546
|
|
# setting with mixed labels
|
|
df = DataFrame({1: [1, 2], 2: [3, 4], "a": ["a", "b"]})
|
|
|
|
result = df.loc[0, [1, 2]]
|
|
expected = Series(
|
|
[1, 3], index=Index([1, 2], dtype=object), dtype=object, name=0
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
expected = DataFrame({1: [5, 2], 2: [6, 4], "a": ["a", "b"]})
|
|
df.loc[0, [1, 2]] = [5, 6]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_frame_multiples(self):
|
|
# multiple setting
|
|
df = DataFrame(
|
|
{"A": ["foo", "bar", "baz"], "B": Series(range(3), dtype=np.int64)}
|
|
)
|
|
rhs = df.loc[1:2]
|
|
rhs.index = df.index[0:2]
|
|
df.loc[0:1] = rhs
|
|
expected = DataFrame(
|
|
{"A": ["bar", "baz", "baz"], "B": Series([1, 2, 2], dtype=np.int64)}
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# multiple setting with frame on rhs (with M8)
|
|
df = DataFrame(
|
|
{
|
|
"date": date_range("2000-01-01", "2000-01-5"),
|
|
"val": Series(range(5), dtype=np.int64),
|
|
}
|
|
)
|
|
expected = DataFrame(
|
|
{
|
|
"date": [
|
|
Timestamp("20000101"),
|
|
Timestamp("20000102"),
|
|
Timestamp("20000101"),
|
|
Timestamp("20000102"),
|
|
Timestamp("20000103"),
|
|
],
|
|
"val": Series([0, 1, 0, 1, 2], dtype=np.int64),
|
|
}
|
|
)
|
|
rhs = df.loc[0:2]
|
|
rhs.index = df.index[2:5]
|
|
df.loc[2:4] = rhs
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"indexer", [["A"], slice(None, "A", None), np.array(["A"])]
|
|
)
|
|
@pytest.mark.parametrize("value", [["Z"], np.array(["Z"])])
|
|
def test_loc_setitem_with_scalar_index(self, indexer, value):
|
|
# GH #19474
|
|
# assigning like "df.loc[0, ['A']] = ['Z']" should be evaluated
|
|
# elementwisely, not using "setter('A', ['Z'])".
|
|
|
|
df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
|
|
df.loc[0, indexer] = value
|
|
result = df.loc[0, "A"]
|
|
|
|
assert is_scalar(result) and result == "Z"
|
|
|
|
@pytest.mark.parametrize(
|
|
"index,box,expected",
|
|
[
|
|
(
|
|
([0, 2], ["A", "B", "C", "D"]),
|
|
7,
|
|
DataFrame(
|
|
[[7, 7, 7, 7], [3, 4, np.nan, np.nan], [7, 7, 7, 7]],
|
|
columns=["A", "B", "C", "D"],
|
|
),
|
|
),
|
|
(
|
|
(1, ["C", "D"]),
|
|
[7, 8],
|
|
DataFrame(
|
|
[[1, 2, np.nan, np.nan], [3, 4, 7, 8], [5, 6, np.nan, np.nan]],
|
|
columns=["A", "B", "C", "D"],
|
|
),
|
|
),
|
|
(
|
|
(1, ["A", "B", "C"]),
|
|
np.array([7, 8, 9], dtype=np.int64),
|
|
DataFrame(
|
|
[[1, 2, np.nan], [7, 8, 9], [5, 6, np.nan]], columns=["A", "B", "C"]
|
|
),
|
|
),
|
|
(
|
|
(slice(1, 3, None), ["B", "C", "D"]),
|
|
[[7, 8, 9], [10, 11, 12]],
|
|
DataFrame(
|
|
[[1, 2, np.nan, np.nan], [3, 7, 8, 9], [5, 10, 11, 12]],
|
|
columns=["A", "B", "C", "D"],
|
|
),
|
|
),
|
|
(
|
|
(slice(1, 3, None), ["C", "A", "D"]),
|
|
np.array([[7, 8, 9], [10, 11, 12]], dtype=np.int64),
|
|
DataFrame(
|
|
[[1, 2, np.nan, np.nan], [8, 4, 7, 9], [11, 6, 10, 12]],
|
|
columns=["A", "B", "C", "D"],
|
|
),
|
|
),
|
|
(
|
|
(slice(None, None, None), ["A", "C"]),
|
|
DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]),
|
|
DataFrame(
|
|
[[7, 2, 8], [9, 4, 10], [11, 6, 12]], columns=["A", "B", "C"]
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_loc_setitem_missing_columns(self, index, box, expected):
|
|
# GH 29334
|
|
df = DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "B"])
|
|
df.loc[index] = box
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_coercion(self):
|
|
|
|
# GH#12411
|
|
df = DataFrame({"date": [Timestamp("20130101").tz_localize("UTC"), pd.NaT]})
|
|
expected = df.dtypes
|
|
|
|
result = df.iloc[[0]]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
result = df.iloc[[1]]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
def test_loc_coercion2(self):
|
|
# GH#12045
|
|
import datetime
|
|
|
|
df = DataFrame(
|
|
{"date": [datetime.datetime(2012, 1, 1), datetime.datetime(1012, 1, 2)]}
|
|
)
|
|
expected = df.dtypes
|
|
|
|
result = df.iloc[[0]]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
result = df.iloc[[1]]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
def test_loc_coercion3(self):
|
|
# GH#11594
|
|
df = DataFrame({"text": ["some words"] + [None] * 9})
|
|
expected = df.dtypes
|
|
|
|
result = df.iloc[0:2]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
result = df.iloc[3:]
|
|
tm.assert_series_equal(result.dtypes, expected)
|
|
|
|
def test_setitem_new_key_tz(self, indexer_sl):
|
|
# GH#12862 should not raise on assigning the second value
|
|
vals = [
|
|
to_datetime(42).tz_localize("UTC"),
|
|
to_datetime(666).tz_localize("UTC"),
|
|
]
|
|
expected = Series(vals, index=["foo", "bar"])
|
|
|
|
ser = Series(dtype=object)
|
|
indexer_sl(ser)["foo"] = vals[0]
|
|
indexer_sl(ser)["bar"] = vals[1]
|
|
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
def test_loc_non_unique(self):
|
|
# GH3659
|
|
# non-unique indexer with loc slice
|
|
# https://groups.google.com/forum/?fromgroups#!topic/pydata/zTm2No0crYs
|
|
|
|
# these are going to raise because the we are non monotonic
|
|
df = DataFrame(
|
|
{"A": [1, 2, 3, 4, 5, 6], "B": [3, 4, 5, 6, 7, 8]}, index=[0, 1, 0, 1, 2, 3]
|
|
)
|
|
msg = "'Cannot get left slice bound for non-unique label: 1'"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[1:]
|
|
msg = "'Cannot get left slice bound for non-unique label: 0'"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[0:]
|
|
msg = "'Cannot get left slice bound for non-unique label: 1'"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[1:2]
|
|
|
|
# monotonic are ok
|
|
df = DataFrame(
|
|
{"A": [1, 2, 3, 4, 5, 6], "B": [3, 4, 5, 6, 7, 8]}, index=[0, 1, 0, 1, 2, 3]
|
|
).sort_index(axis=0)
|
|
result = df.loc[1:]
|
|
expected = DataFrame({"A": [2, 4, 5, 6], "B": [4, 6, 7, 8]}, index=[1, 1, 2, 3])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.loc[0:]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.loc[1:2]
|
|
expected = DataFrame({"A": [2, 4, 5], "B": [4, 6, 7]}, index=[1, 1, 2])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.arm_slow
|
|
def test_loc_non_unique_memory_error(self):
|
|
|
|
# GH 4280
|
|
# non_unique index with a large selection triggers a memory error
|
|
|
|
columns = list("ABCDEFG")
|
|
|
|
def gen_test(length, l2):
|
|
return pd.concat(
|
|
[
|
|
DataFrame(
|
|
np.random.randn(length, len(columns)),
|
|
index=np.arange(length),
|
|
columns=columns,
|
|
),
|
|
DataFrame(
|
|
np.ones((l2, len(columns))), index=[0] * l2, columns=columns
|
|
),
|
|
]
|
|
)
|
|
|
|
def gen_expected(df, mask):
|
|
len_mask = len(mask)
|
|
return pd.concat(
|
|
[
|
|
df.take([0]),
|
|
DataFrame(
|
|
np.ones((len_mask, len(columns))),
|
|
index=[0] * len_mask,
|
|
columns=columns,
|
|
),
|
|
df.take(mask[1:]),
|
|
]
|
|
)
|
|
|
|
df = gen_test(900, 100)
|
|
assert df.index.is_unique is False
|
|
|
|
mask = np.arange(100)
|
|
result = df.loc[mask]
|
|
expected = gen_expected(df, mask)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df = gen_test(900000, 100000)
|
|
assert df.index.is_unique is False
|
|
|
|
mask = np.arange(100000)
|
|
result = df.loc[mask]
|
|
expected = gen_expected(df, mask)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_name(self):
|
|
# GH 3880
|
|
df = DataFrame([[1, 1], [1, 1]])
|
|
df.index.name = "index_name"
|
|
result = df.iloc[[0, 1]].index.name
|
|
assert result == "index_name"
|
|
|
|
result = df.loc[[0, 1]].index.name
|
|
assert result == "index_name"
|
|
|
|
def test_loc_empty_list_indexer_is_ok(self):
|
|
|
|
df = tm.makeCustomDataframe(5, 2)
|
|
# vertical empty
|
|
tm.assert_frame_equal(
|
|
df.loc[:, []], df.iloc[:, :0], check_index_type=True, check_column_type=True
|
|
)
|
|
# horizontal empty
|
|
tm.assert_frame_equal(
|
|
df.loc[[], :], df.iloc[:0, :], check_index_type=True, check_column_type=True
|
|
)
|
|
# horizontal empty
|
|
tm.assert_frame_equal(
|
|
df.loc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True
|
|
)
|
|
|
|
def test_identity_slice_returns_new_object(self, using_array_manager, request):
|
|
# GH13873
|
|
if using_array_manager:
|
|
mark = pytest.mark.xfail(
|
|
reason="setting with .loc[:, 'a'] does not alter inplace"
|
|
)
|
|
request.node.add_marker(mark)
|
|
|
|
original_df = DataFrame({"a": [1, 2, 3]})
|
|
sliced_df = original_df.loc[:]
|
|
assert sliced_df is not original_df
|
|
assert original_df[:] is not original_df
|
|
|
|
# should be a shallow copy
|
|
assert np.shares_memory(original_df["a"]._values, sliced_df["a"]._values)
|
|
|
|
# Setting using .loc[:, "a"] sets inplace so alters both sliced and orig
|
|
original_df.loc[:, "a"] = [4, 4, 4]
|
|
assert (sliced_df["a"] == 4).all()
|
|
|
|
# These should not return copies
|
|
assert original_df is original_df.loc[:, :]
|
|
df = DataFrame(np.random.randn(10, 4))
|
|
assert df[0] is df.loc[:, 0]
|
|
|
|
# Same tests for Series
|
|
original_series = Series([1, 2, 3, 4, 5, 6])
|
|
sliced_series = original_series.loc[:]
|
|
assert sliced_series is not original_series
|
|
assert original_series[:] is not original_series
|
|
|
|
original_series[:3] = [7, 8, 9]
|
|
assert all(sliced_series[:3] == [7, 8, 9])
|
|
|
|
@pytest.mark.xfail(reason="accidental fix reverted - GH37497")
|
|
def test_loc_copy_vs_view(self):
|
|
# GH 15631
|
|
x = DataFrame(zip(range(3), range(3)), columns=["a", "b"])
|
|
|
|
y = x.copy()
|
|
q = y.loc[:, "a"]
|
|
q += 2
|
|
|
|
tm.assert_frame_equal(x, y)
|
|
|
|
z = x.copy()
|
|
q = z.loc[x.index, "a"]
|
|
q += 2
|
|
|
|
tm.assert_frame_equal(x, z)
|
|
|
|
def test_loc_uint64(self):
|
|
# GH20722
|
|
# Test whether loc accept uint64 max value as index.
|
|
umax = np.iinfo("uint64").max
|
|
ser = Series([1, 2], index=[umax - 1, umax])
|
|
|
|
result = ser.loc[umax - 1]
|
|
expected = ser.iloc[0]
|
|
assert result == expected
|
|
|
|
result = ser.loc[[umax - 1]]
|
|
expected = ser.iloc[[0]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = ser.loc[[umax - 1, umax]]
|
|
tm.assert_series_equal(result, ser)
|
|
|
|
def test_loc_uint64_disallow_negative(self):
|
|
# GH#41775
|
|
umax = np.iinfo("uint64").max
|
|
ser = Series([1, 2], index=[umax - 1, umax])
|
|
|
|
with pytest.raises(KeyError, match="-1"):
|
|
# don't wrap around
|
|
ser.loc[-1]
|
|
|
|
with pytest.raises(KeyError, match="-1"):
|
|
# don't wrap around
|
|
ser.loc[[-1]]
|
|
|
|
def test_loc_setitem_empty_append_expands_rows(self):
|
|
# GH6173, various appends to an empty dataframe
|
|
|
|
data = [1, 2, 3]
|
|
expected = DataFrame({"x": data, "y": [None] * len(data)})
|
|
|
|
# appends to fit length of data
|
|
df = DataFrame(columns=["x", "y"])
|
|
df.loc[:, "x"] = data
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_empty_append_expands_rows_mixed_dtype(self):
|
|
# GH#37932 same as test_loc_setitem_empty_append_expands_rows
|
|
# but with mixed dtype so we go through take_split_path
|
|
data = [1, 2, 3]
|
|
expected = DataFrame({"x": data, "y": [None] * len(data)})
|
|
|
|
df = DataFrame(columns=["x", "y"])
|
|
df["x"] = df["x"].astype(np.int64)
|
|
df.loc[:, "x"] = data
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_empty_append_single_value(self):
|
|
# only appends one value
|
|
expected = DataFrame({"x": [1.0], "y": [np.nan]})
|
|
df = DataFrame(columns=["x", "y"], dtype=float)
|
|
df.loc[0, "x"] = expected.loc[0, "x"]
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_empty_append_raises(self):
|
|
# GH6173, various appends to an empty dataframe
|
|
|
|
data = [1, 2]
|
|
df = DataFrame(columns=["x", "y"])
|
|
df.index = df.index.astype(np.int64)
|
|
msg = (
|
|
r"None of \[Int64Index\(\[0, 1\], dtype='int64'\)\] "
|
|
r"are in the \[index\]"
|
|
)
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[[0, 1], "x"] = data
|
|
|
|
msg = "|".join(
|
|
[
|
|
"cannot copy sequence with size 2 to array axis with dimension 0",
|
|
r"could not broadcast input array from shape \(2,\) into shape \(0,\)",
|
|
"Must have equal len keys and value when setting with an iterable",
|
|
]
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.loc[0:2, "x"] = data
|
|
|
|
def test_indexing_zerodim_np_array(self):
|
|
# GH24924
|
|
df = DataFrame([[1, 2], [3, 4]])
|
|
result = df.loc[np.array(0)]
|
|
s = Series([1, 2], name=0)
|
|
tm.assert_series_equal(result, s)
|
|
|
|
def test_series_indexing_zerodim_np_array(self):
|
|
# GH24924
|
|
s = Series([1, 2])
|
|
result = s.loc[np.array(0)]
|
|
assert result == 1
|
|
|
|
def test_loc_reverse_assignment(self):
|
|
# GH26939
|
|
data = [1, 2, 3, 4, 5, 6] + [None] * 4
|
|
expected = Series(data, index=range(2010, 2020))
|
|
|
|
result = Series(index=range(2010, 2020), dtype=np.float64)
|
|
result.loc[2015:2010:-1] = [6, 5, 4, 3, 2, 1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_str_to_small_float_conversion_type(self):
|
|
# GH#20388
|
|
np.random.seed(13)
|
|
col_data = [str(np.random.random() * 1e-12) for _ in range(5)]
|
|
result = DataFrame(col_data, columns=["A"])
|
|
expected = DataFrame(col_data, columns=["A"], dtype=object)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# assigning with loc/iloc attempts to set the values inplace, which
|
|
# in this case is successful
|
|
result.loc[result.index, "A"] = [float(x) for x in col_data]
|
|
expected = DataFrame(col_data, columns=["A"], dtype=float).astype(object)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# assigning the entire column using __setitem__ swaps in the new array
|
|
# GH#???
|
|
result["A"] = [float(x) for x in col_data]
|
|
expected = DataFrame(col_data, columns=["A"], dtype=float)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_time_object(self, frame_or_series):
|
|
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
|
|
mask = (rng.hour == 9) & (rng.minute == 30)
|
|
|
|
obj = DataFrame(np.random.randn(len(rng), 3), index=rng)
|
|
obj = tm.get_obj(obj, frame_or_series)
|
|
|
|
result = obj.loc[time(9, 30)]
|
|
exp = obj.loc[mask]
|
|
tm.assert_equal(result, exp)
|
|
|
|
chunk = obj.loc["1/4/2000":]
|
|
result = chunk.loc[time(9, 30)]
|
|
expected = result[-1:]
|
|
|
|
# Without resetting the freqs, these are 5 min and 1440 min, respectively
|
|
result.index = result.index._with_freq(None)
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("spmatrix_t", ["coo_matrix", "csc_matrix", "csr_matrix"])
|
|
@pytest.mark.parametrize("dtype", [np.int64, np.float64, complex])
|
|
@td.skip_if_no_scipy
|
|
def test_loc_getitem_range_from_spmatrix(self, spmatrix_t, dtype):
|
|
import scipy.sparse
|
|
|
|
spmatrix_t = getattr(scipy.sparse, spmatrix_t)
|
|
|
|
# The bug is triggered by a sparse matrix with purely sparse columns. So the
|
|
# recipe below generates a rectangular matrix of dimension (5, 7) where all the
|
|
# diagonal cells are ones, meaning the last two columns are purely sparse.
|
|
rows, cols = 5, 7
|
|
spmatrix = spmatrix_t(np.eye(rows, cols, dtype=dtype), dtype=dtype)
|
|
df = DataFrame.sparse.from_spmatrix(spmatrix)
|
|
|
|
# regression test for GH#34526
|
|
itr_idx = range(2, rows)
|
|
result = df.loc[itr_idx].values
|
|
expected = spmatrix.toarray()[itr_idx]
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
# regression test for GH#34540
|
|
result = df.loc[itr_idx].dtypes.values
|
|
expected = np.full(cols, SparseDtype(dtype, fill_value=0))
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_loc_getitem_listlike_all_retains_sparse(self):
|
|
df = DataFrame({"A": pd.array([0, 0], dtype=SparseDtype("int64"))})
|
|
result = df.loc[[0, 1]]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_loc_getitem_sparse_frame(self):
|
|
# GH34687
|
|
from scipy.sparse import eye
|
|
|
|
df = DataFrame.sparse.from_spmatrix(eye(5))
|
|
result = df.loc[range(2)]
|
|
expected = DataFrame(
|
|
[[1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0]],
|
|
dtype=SparseDtype("float64", 0.0),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.loc[range(2)].loc[range(1)]
|
|
expected = DataFrame(
|
|
[[1.0, 0.0, 0.0, 0.0, 0.0]], dtype=SparseDtype("float64", 0.0)
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_sparse_series(self):
|
|
# GH34687
|
|
s = Series([1.0, 0.0, 0.0, 0.0, 0.0], dtype=SparseDtype("float64", 0.0))
|
|
|
|
result = s.loc[range(2)]
|
|
expected = Series([1.0, 0.0], dtype=SparseDtype("float64", 0.0))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = s.loc[range(3)].loc[range(2)]
|
|
expected = Series([1.0, 0.0], dtype=SparseDtype("float64", 0.0))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("key_type", [iter, np.array, Series, Index])
|
|
def test_loc_getitem_iterable(self, float_frame, key_type):
|
|
idx = key_type(["A", "B", "C"])
|
|
result = float_frame.loc[:, idx]
|
|
expected = float_frame.loc[:, ["A", "B", "C"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_timedelta_0seconds(self):
|
|
# GH#10583
|
|
df = DataFrame(np.random.normal(size=(10, 4)))
|
|
df.index = timedelta_range(start="0s", periods=10, freq="s")
|
|
expected = df.loc[Timedelta("0s") :, :]
|
|
result = df.loc["0s":, :]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"val,expected", [(2**63 - 1, Series([1])), (2**63, Series([2]))]
|
|
)
|
|
def test_loc_getitem_uint64_scalar(self, val, expected):
|
|
# see GH#19399
|
|
df = DataFrame([1, 2], index=[2**63 - 1, 2**63])
|
|
result = df.loc[val]
|
|
|
|
expected.name = val
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_int_label_with_float64index(self):
|
|
# note labels are floats
|
|
ser = Series(["a", "b", "c"], index=[0, 0.5, 1])
|
|
expected = ser.copy()
|
|
|
|
ser.loc[1] = "zoo"
|
|
expected.iloc[2] = "zoo"
|
|
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"indexer, expected",
|
|
[
|
|
# The test name is a misnomer in the 0 case as df.index[indexer]
|
|
# is a scalar.
|
|
(0, [20, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
|
|
(slice(4, 8), [0, 1, 2, 3, 20, 20, 20, 20, 8, 9]),
|
|
([3, 5], [0, 1, 2, 20, 4, 20, 6, 7, 8, 9]),
|
|
],
|
|
)
|
|
def test_loc_setitem_listlike_with_timedelta64index(self, indexer, expected):
|
|
# GH#16637
|
|
tdi = to_timedelta(range(10), unit="s")
|
|
df = DataFrame({"x": range(10)}, dtype="int64", index=tdi)
|
|
|
|
df.loc[df.index[indexer], "x"] = 20
|
|
|
|
expected = DataFrame(
|
|
expected,
|
|
index=tdi,
|
|
columns=["x"],
|
|
dtype="int64",
|
|
)
|
|
|
|
tm.assert_frame_equal(expected, df)
|
|
|
|
def test_loc_setitem_categorical_values_partial_column_slice(self):
|
|
# Assigning a Category to parts of a int/... column uses the values of
|
|
# the Categorical
|
|
df = DataFrame({"a": [1, 1, 1, 1, 1], "b": list("aaaaa")})
|
|
exp = DataFrame({"a": [1, "b", "b", 1, 1], "b": list("aabba")})
|
|
df.loc[1:2, "a"] = Categorical(["b", "b"], categories=["a", "b"])
|
|
df.loc[2:3, "b"] = Categorical(["b", "b"], categories=["a", "b"])
|
|
tm.assert_frame_equal(df, exp)
|
|
|
|
def test_loc_setitem_single_row_categorical(self):
|
|
# GH#25495
|
|
df = DataFrame({"Alpha": ["a"], "Numeric": [0]})
|
|
categories = Categorical(df["Alpha"], categories=["a", "b", "c"])
|
|
df.loc[:, "Alpha"] = categories
|
|
|
|
result = df["Alpha"]
|
|
expected = Series(categories, index=df.index, name="Alpha")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_datetime_coercion(self):
|
|
# GH#1048
|
|
df = DataFrame({"c": [Timestamp("2010-10-01")] * 3})
|
|
df.loc[0:1, "c"] = np.datetime64("2008-08-08")
|
|
assert Timestamp("2008-08-08") == df.loc[0, "c"]
|
|
assert Timestamp("2008-08-08") == df.loc[1, "c"]
|
|
df.loc[2, "c"] = date(2005, 5, 5)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
# Comparing Timestamp to date obj is deprecated
|
|
assert Timestamp("2005-05-05") == df.loc[2, "c"]
|
|
assert Timestamp("2005-05-05").date() == df.loc[2, "c"]
|
|
|
|
@pytest.mark.parametrize("idxer", ["var", ["var"]])
|
|
def test_loc_setitem_datetimeindex_tz(self, idxer, tz_naive_fixture):
|
|
# GH#11365
|
|
tz = tz_naive_fixture
|
|
idx = date_range(start="2015-07-12", periods=3, freq="H", tz=tz)
|
|
expected = DataFrame(1.2, index=idx, columns=["var"])
|
|
# if result started off with object dtype, then the .loc.__setitem__
|
|
# below would retain object dtype
|
|
result = DataFrame(index=idx, columns=["var"], dtype=np.float64)
|
|
result.loc[:, idxer] = expected
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_setitem_time_key(self, using_array_manager):
|
|
index = date_range("2012-01-01", "2012-01-05", freq="30min")
|
|
df = DataFrame(np.random.randn(len(index), 5), index=index)
|
|
akey = time(12, 0, 0)
|
|
bkey = slice(time(13, 0, 0), time(14, 0, 0))
|
|
ainds = [24, 72, 120, 168]
|
|
binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172]
|
|
|
|
result = df.copy()
|
|
result.loc[akey] = 0
|
|
result = result.loc[akey]
|
|
expected = df.loc[akey].copy()
|
|
expected.loc[:] = 0
|
|
if using_array_manager:
|
|
# TODO(ArrayManager) we are still overwriting columns
|
|
expected = expected.astype(float)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[akey] = 0
|
|
result.loc[akey] = df.iloc[ainds]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.copy()
|
|
result.loc[bkey] = 0
|
|
result = result.loc[bkey]
|
|
expected = df.loc[bkey].copy()
|
|
expected.loc[:] = 0
|
|
if using_array_manager:
|
|
# TODO(ArrayManager) we are still overwriting columns
|
|
expected = expected.astype(float)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[bkey] = 0
|
|
result.loc[bkey] = df.iloc[binds]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
@pytest.mark.parametrize("key", ["A", ["A"], ("A", slice(None))])
|
|
def test_loc_setitem_unsorted_multiindex_columns(self, key):
|
|
# GH#38601
|
|
mi = MultiIndex.from_tuples([("A", 4), ("B", "3"), ("A", "2")])
|
|
df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi)
|
|
obj = df.copy()
|
|
obj.loc[:, key] = np.zeros((2, 2), dtype=int)
|
|
expected = DataFrame([[0, 2, 0], [0, 5, 0]], columns=mi)
|
|
tm.assert_frame_equal(obj, expected)
|
|
|
|
df = df.sort_index(axis=1)
|
|
df.loc[:, key] = np.zeros((2, 2), dtype=int)
|
|
expected = expected.sort_index(axis=1)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_uint_drop(self, any_int_numpy_dtype):
|
|
# see GH#18311
|
|
# assigning series.loc[0] = 4 changed series.dtype to int
|
|
series = Series([1, 2, 3], dtype=any_int_numpy_dtype)
|
|
series.loc[0] = 4
|
|
expected = Series([4, 2, 3], dtype=any_int_numpy_dtype)
|
|
tm.assert_series_equal(series, expected)
|
|
|
|
def test_loc_setitem_td64_non_nano(self):
|
|
# GH#14155
|
|
ser = Series(10 * [np.timedelta64(10, "m")])
|
|
ser.loc[[1, 2, 3]] = np.timedelta64(20, "m")
|
|
expected = Series(10 * [np.timedelta64(10, "m")])
|
|
expected.loc[[1, 2, 3]] = Timedelta(np.timedelta64(20, "m"))
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
def test_loc_setitem_2d_to_1d_raises(self):
|
|
data = np.random.randn(2, 2)
|
|
ser = Series(range(2))
|
|
|
|
msg = "|".join(
|
|
[
|
|
r"shape mismatch: value array of shape \(2,2\)",
|
|
r"cannot reshape array of size 4 into shape \(2,\)",
|
|
]
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser.loc[range(2)] = data
|
|
|
|
msg = r"could not broadcast input array from shape \(2,2\) into shape \(2,?\)"
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser.loc[:] = data
|
|
|
|
def test_loc_getitem_interval_index(self):
|
|
# GH#19977
|
|
index = pd.interval_range(start=0, periods=3)
|
|
df = DataFrame(
|
|
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"]
|
|
)
|
|
|
|
expected = 1
|
|
result = df.loc[0.5, "A"]
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
def test_loc_getitem_interval_index2(self):
|
|
# GH#19977
|
|
index = pd.interval_range(start=0, periods=3, closed="both")
|
|
df = DataFrame(
|
|
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"]
|
|
)
|
|
|
|
index_exp = pd.interval_range(start=0, periods=2, freq=1, closed="both")
|
|
expected = Series([1, 4], index=index_exp, name="A")
|
|
result = df.loc[1, "A"]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("tpl", [(1,), (1, 2)])
|
|
def test_loc_getitem_index_single_double_tuples(self, tpl):
|
|
# GH#20991
|
|
idx = Index(
|
|
[(1,), (1, 2)],
|
|
name="A",
|
|
tupleize_cols=False,
|
|
)
|
|
df = DataFrame(index=idx)
|
|
|
|
result = df.loc[[tpl]]
|
|
idx = Index([tpl], name="A", tupleize_cols=False)
|
|
expected = DataFrame(index=idx)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_index_namedtuple(self):
|
|
IndexType = namedtuple("IndexType", ["a", "b"])
|
|
idx1 = IndexType("foo", "bar")
|
|
idx2 = IndexType("baz", "bof")
|
|
index = Index([idx1, idx2], name="composite_index", tupleize_cols=False)
|
|
df = DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"])
|
|
|
|
result = df.loc[IndexType("foo", "bar")]["A"]
|
|
assert result == 1
|
|
|
|
def test_loc_setitem_single_column_mixed(self):
|
|
df = DataFrame(
|
|
np.random.randn(5, 3),
|
|
index=["a", "b", "c", "d", "e"],
|
|
columns=["foo", "bar", "baz"],
|
|
)
|
|
df["str"] = "qux"
|
|
df.loc[df.index[::2], "str"] = np.nan
|
|
expected = np.array([np.nan, "qux", np.nan, "qux", np.nan], dtype=object)
|
|
tm.assert_almost_equal(df["str"].values, expected)
|
|
|
|
def test_loc_setitem_cast2(self):
|
|
# GH#7704
|
|
# dtype conversion on setting
|
|
df = DataFrame(np.random.rand(30, 3), columns=tuple("ABC"))
|
|
df["event"] = np.nan
|
|
df.loc[10, "event"] = "foo"
|
|
result = df.dtypes
|
|
expected = Series(
|
|
[np.dtype("float64")] * 3 + [np.dtype("object")],
|
|
index=["A", "B", "C", "event"],
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_cast3(self):
|
|
# Test that data type is preserved . GH#5782
|
|
df = DataFrame({"one": np.arange(6, dtype=np.int8)})
|
|
df.loc[1, "one"] = 6
|
|
assert df.dtypes.one == np.dtype(np.int8)
|
|
df.one = np.int8(7)
|
|
assert df.dtypes.one == np.dtype(np.int8)
|
|
|
|
|
|
class TestLocWithEllipsis:
|
|
@pytest.fixture(params=[tm.loc, tm.iloc])
|
|
def indexer(self, request):
|
|
# Test iloc while we're here
|
|
return request.param
|
|
|
|
@pytest.fixture
|
|
def obj(self, series_with_simple_index, frame_or_series):
|
|
obj = series_with_simple_index
|
|
if frame_or_series is not Series:
|
|
obj = obj.to_frame()
|
|
return obj
|
|
|
|
def test_loc_iloc_getitem_ellipsis(self, obj, indexer):
|
|
result = indexer(obj)[...]
|
|
tm.assert_equal(result, obj)
|
|
|
|
def test_loc_iloc_getitem_leading_ellipses(self, series_with_simple_index, indexer):
|
|
obj = series_with_simple_index
|
|
key = 0 if (indexer is tm.iloc or len(obj) == 0) else obj.index[0]
|
|
|
|
if indexer is tm.loc and obj.index.is_boolean():
|
|
# passing [False] will get interpreted as a boolean mask
|
|
# TODO: should it? unambiguous when lengths dont match?
|
|
return
|
|
if indexer is tm.loc and isinstance(obj.index, MultiIndex):
|
|
msg = "MultiIndex does not support indexing with Ellipsis"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
result = indexer(obj)[..., [key]]
|
|
|
|
elif len(obj) != 0:
|
|
result = indexer(obj)[..., [key]]
|
|
expected = indexer(obj)[[key]]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
key2 = 0 if indexer is tm.iloc else obj.name
|
|
df = obj.to_frame()
|
|
result = indexer(df)[..., [key2]]
|
|
expected = indexer(df)[:, [key2]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_iloc_getitem_ellipses_only_one_ellipsis(self, obj, indexer):
|
|
# GH37750
|
|
key = 0 if (indexer is tm.iloc or len(obj) == 0) else obj.index[0]
|
|
|
|
with pytest.raises(IndexingError, match=_one_ellipsis_message):
|
|
indexer(obj)[..., ...]
|
|
|
|
with pytest.raises(IndexingError, match=_one_ellipsis_message):
|
|
indexer(obj)[..., [key], ...]
|
|
|
|
with pytest.raises(IndexingError, match=_one_ellipsis_message):
|
|
indexer(obj)[..., ..., key]
|
|
|
|
# one_ellipsis_message takes precedence over "Too many indexers"
|
|
# only when the first key is Ellipsis
|
|
with pytest.raises(IndexingError, match="Too many indexers"):
|
|
indexer(obj)[key, ..., ...]
|
|
|
|
|
|
class TestLocWithMultiIndex:
|
|
@pytest.mark.parametrize(
|
|
"keys, expected",
|
|
[
|
|
(["b", "a"], [["b", "b", "a", "a"], [1, 2, 1, 2]]),
|
|
(["a", "b"], [["a", "a", "b", "b"], [1, 2, 1, 2]]),
|
|
((["a", "b"], [1, 2]), [["a", "a", "b", "b"], [1, 2, 1, 2]]),
|
|
((["a", "b"], [2, 1]), [["a", "a", "b", "b"], [2, 1, 2, 1]]),
|
|
((["b", "a"], [2, 1]), [["b", "b", "a", "a"], [2, 1, 2, 1]]),
|
|
((["b", "a"], [1, 2]), [["b", "b", "a", "a"], [1, 2, 1, 2]]),
|
|
((["c", "a"], [2, 1]), [["c", "a", "a"], [1, 2, 1]]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("dim", ["index", "columns"])
|
|
def test_loc_getitem_multilevel_index_order(self, dim, keys, expected):
|
|
# GH#22797
|
|
# Try to respect order of keys given for MultiIndex.loc
|
|
kwargs = {dim: [["c", "a", "a", "b", "b"], [1, 1, 2, 1, 2]]}
|
|
df = DataFrame(np.arange(25).reshape(5, 5), **kwargs)
|
|
exp_index = MultiIndex.from_arrays(expected)
|
|
if dim == "index":
|
|
res = df.loc[keys, :]
|
|
tm.assert_index_equal(res.index, exp_index)
|
|
elif dim == "columns":
|
|
res = df.loc[:, keys]
|
|
tm.assert_index_equal(res.columns, exp_index)
|
|
|
|
def test_loc_preserve_names(self, multiindex_year_month_day_dataframe_random_data):
|
|
ymd = multiindex_year_month_day_dataframe_random_data
|
|
|
|
result = ymd.loc[2000]
|
|
result2 = ymd["A"].loc[2000]
|
|
assert result.index.names == ymd.index.names[1:]
|
|
assert result2.index.names == ymd.index.names[1:]
|
|
|
|
result = ymd.loc[2000, 2]
|
|
result2 = ymd["A"].loc[2000, 2]
|
|
assert result.index.name == ymd.index.names[2]
|
|
assert result2.index.name == ymd.index.names[2]
|
|
|
|
def test_loc_getitem_multiindex_nonunique_len_zero(self):
|
|
# GH#13691
|
|
mi = MultiIndex.from_product([[0], [1, 1]])
|
|
ser = Series(0, index=mi)
|
|
|
|
res = ser.loc[[]]
|
|
|
|
expected = ser[:0]
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
res2 = ser.loc[ser.iloc[0:0]]
|
|
tm.assert_series_equal(res2, expected)
|
|
|
|
def test_loc_getitem_access_none_value_in_multiindex(self):
|
|
# GH#34318: test that you can access a None value using .loc
|
|
# through a Multiindex
|
|
|
|
ser = Series([None], MultiIndex.from_arrays([["Level1"], ["Level2"]]))
|
|
result = ser.loc[("Level1", "Level2")]
|
|
assert result is None
|
|
|
|
midx = MultiIndex.from_product([["Level1"], ["Level2_a", "Level2_b"]])
|
|
ser = Series([None] * len(midx), dtype=object, index=midx)
|
|
result = ser.loc[("Level1", "Level2_a")]
|
|
assert result is None
|
|
|
|
ser = Series([1] * len(midx), dtype=object, index=midx)
|
|
result = ser.loc[("Level1", "Level2_a")]
|
|
assert result == 1
|
|
|
|
def test_loc_setitem_multiindex_slice(self):
|
|
# GH 34870
|
|
|
|
index = MultiIndex.from_tuples(
|
|
zip(
|
|
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
|
|
["one", "two", "one", "two", "one", "two", "one", "two"],
|
|
),
|
|
names=["first", "second"],
|
|
)
|
|
|
|
result = Series([1, 1, 1, 1, 1, 1, 1, 1], index=index)
|
|
result.loc[("baz", "one"):("foo", "two")] = 100
|
|
|
|
expected = Series([1, 1, 100, 100, 100, 100, 1, 1], index=index)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_getitem_slice_datetime_objs_with_datetimeindex(self):
|
|
times = date_range("2000-01-01", freq="10min", periods=100000)
|
|
ser = Series(range(100000), times)
|
|
result = ser.loc[datetime(1900, 1, 1) : datetime(2100, 1, 1)]
|
|
tm.assert_series_equal(result, ser)
|
|
|
|
def test_loc_getitem_datetime_string_with_datetimeindex(self):
|
|
# GH 16710
|
|
df = DataFrame(
|
|
{"a": range(10), "b": range(10)},
|
|
index=date_range("2010-01-01", "2010-01-10"),
|
|
)
|
|
result = df.loc[["2010-01-01", "2010-01-05"], ["a", "b"]]
|
|
expected = DataFrame(
|
|
{"a": [0, 4], "b": [0, 4]},
|
|
index=DatetimeIndex(["2010-01-01", "2010-01-05"]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_sorted_index_level_with_duplicates(self):
|
|
# GH#4516 sorting a MultiIndex with duplicates and multiple dtypes
|
|
mi = MultiIndex.from_tuples(
|
|
[
|
|
("foo", "bar"),
|
|
("foo", "bar"),
|
|
("bah", "bam"),
|
|
("bah", "bam"),
|
|
("foo", "bar"),
|
|
("bah", "bam"),
|
|
],
|
|
names=["A", "B"],
|
|
)
|
|
df = DataFrame(
|
|
[
|
|
[1.0, 1],
|
|
[2.0, 2],
|
|
[3.0, 3],
|
|
[4.0, 4],
|
|
[5.0, 5],
|
|
[6.0, 6],
|
|
],
|
|
index=mi,
|
|
columns=["C", "D"],
|
|
)
|
|
df = df.sort_index(level=0)
|
|
|
|
expected = DataFrame(
|
|
[[1.0, 1], [2.0, 2], [5.0, 5]], columns=["C", "D"], index=mi.take([0, 1, 4])
|
|
)
|
|
|
|
result = df.loc[("foo", "bar")]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_preserves_index_level_category_dtype(self):
|
|
# GH#15166
|
|
df = DataFrame(
|
|
data=np.arange(2, 22, 2),
|
|
index=MultiIndex(
|
|
levels=[CategoricalIndex(["a", "b"]), range(10)],
|
|
codes=[[0] * 5 + [1] * 5, range(10)],
|
|
names=["Index1", "Index2"],
|
|
),
|
|
)
|
|
|
|
expected = CategoricalIndex(
|
|
["a", "b"],
|
|
categories=["a", "b"],
|
|
ordered=False,
|
|
name="Index1",
|
|
dtype="category",
|
|
)
|
|
|
|
result = df.index.levels[0]
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
result = df.loc[["a"]].index.levels[0]
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("lt_value", [30, 10])
|
|
def test_loc_multiindex_levels_contain_values_not_in_index_anymore(self, lt_value):
|
|
# GH#41170
|
|
df = DataFrame({"a": [12, 23, 34, 45]}, index=[list("aabb"), [0, 1, 2, 3]])
|
|
with pytest.raises(KeyError, match=r"\['b'\] not in index"):
|
|
df.loc[df["a"] < lt_value, :].loc[["b"], :]
|
|
|
|
def test_loc_multiindex_null_slice_na_level(self):
|
|
# GH#42055
|
|
lev1 = np.array([np.nan, np.nan])
|
|
lev2 = ["bar", "baz"]
|
|
mi = MultiIndex.from_arrays([lev1, lev2])
|
|
ser = Series([0, 1], index=mi)
|
|
result = ser.loc[:, "bar"]
|
|
|
|
# TODO: should we have name="bar"?
|
|
expected = Series([0], index=[np.nan])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_drops_level(self):
|
|
# Based on test_series_varied_multiindex_alignment, where
|
|
# this used to fail to drop the first level
|
|
mi = MultiIndex.from_product(
|
|
[list("ab"), list("xy"), [1, 2]], names=["ab", "xy", "num"]
|
|
)
|
|
ser = Series(range(8), index=mi)
|
|
|
|
loc_result = ser.loc["a", :, :]
|
|
expected = ser.index.droplevel(0)[:4]
|
|
tm.assert_index_equal(loc_result.index, expected)
|
|
|
|
|
|
class TestLocSetitemWithExpansion:
|
|
@pytest.mark.slow
|
|
def test_loc_setitem_with_expansion_large_dataframe(self):
|
|
# GH#10692
|
|
result = DataFrame({"x": range(10**6)}, dtype="int64")
|
|
result.loc[len(result)] = len(result) + 1
|
|
expected = DataFrame({"x": range(10**6 + 1)}, dtype="int64")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_setitem_empty_series(self):
|
|
# GH#5226
|
|
|
|
# partially set with an empty object series
|
|
ser = Series(dtype=object)
|
|
ser.loc[1] = 1
|
|
tm.assert_series_equal(ser, Series([1], index=[1]))
|
|
ser.loc[3] = 3
|
|
tm.assert_series_equal(ser, Series([1, 3], index=[1, 3]))
|
|
|
|
ser = Series(dtype=object)
|
|
ser.loc[1] = 1.0
|
|
tm.assert_series_equal(ser, Series([1.0], index=[1]))
|
|
ser.loc[3] = 3.0
|
|
tm.assert_series_equal(ser, Series([1.0, 3.0], index=[1, 3]))
|
|
|
|
ser = Series(dtype=object)
|
|
ser.loc["foo"] = 1
|
|
tm.assert_series_equal(ser, Series([1], index=["foo"]))
|
|
ser.loc["bar"] = 3
|
|
tm.assert_series_equal(ser, Series([1, 3], index=["foo", "bar"]))
|
|
ser.loc[3] = 4
|
|
tm.assert_series_equal(ser, Series([1, 3, 4], index=["foo", "bar", 3]))
|
|
|
|
def test_loc_setitem_incremental_with_dst(self):
|
|
# GH#20724
|
|
base = datetime(2015, 11, 1, tzinfo=gettz("US/Pacific"))
|
|
idxs = [base + timedelta(seconds=i * 900) for i in range(16)]
|
|
result = Series([0], index=[idxs[0]])
|
|
for ts in idxs:
|
|
result.loc[ts] = 1
|
|
expected = Series(1, index=idxs)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_setitem_datetime_keys_cast(self):
|
|
# GH#9516
|
|
dt1 = Timestamp("20130101 09:00:00")
|
|
dt2 = Timestamp("20130101 10:00:00")
|
|
|
|
for conv in [
|
|
lambda x: x,
|
|
lambda x: x.to_datetime64(),
|
|
lambda x: x.to_pydatetime(),
|
|
lambda x: np.datetime64(x),
|
|
]:
|
|
|
|
df = DataFrame()
|
|
df.loc[conv(dt1), "one"] = 100
|
|
df.loc[conv(dt2), "one"] = 200
|
|
|
|
expected = DataFrame({"one": [100.0, 200.0]}, index=[dt1, dt2])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_categorical_column_retains_dtype(self, ordered):
|
|
# GH16360
|
|
result = DataFrame({"A": [1]})
|
|
result.loc[:, "B"] = Categorical(["b"], ordered=ordered)
|
|
expected = DataFrame({"A": [1], "B": Categorical(["b"], ordered=ordered)})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_setitem_with_expansion_and_existing_dst(self):
|
|
# GH#18308
|
|
start = Timestamp("2017-10-29 00:00:00+0200", tz="Europe/Madrid")
|
|
end = Timestamp("2017-10-29 03:00:00+0100", tz="Europe/Madrid")
|
|
ts = Timestamp("2016-10-10 03:00:00", tz="Europe/Madrid")
|
|
idx = date_range(start, end, inclusive="left", freq="H")
|
|
assert ts not in idx # i.e. result.loc setitem is with-expansion
|
|
|
|
result = DataFrame(index=idx, columns=["value"])
|
|
result.loc[ts, "value"] = 12
|
|
expected = DataFrame(
|
|
[np.nan] * len(idx) + [12],
|
|
index=idx.append(DatetimeIndex([ts])),
|
|
columns=["value"],
|
|
dtype=object,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_setitem_with_expansion(self):
|
|
# indexing - setting an element
|
|
df = DataFrame(
|
|
data=to_datetime(["2015-03-30 20:12:32", "2015-03-12 00:11:11"]),
|
|
columns=["time"],
|
|
)
|
|
df["new_col"] = ["new", "old"]
|
|
df.time = df.set_index("time").index.tz_localize("UTC")
|
|
v = df[df.new_col == "new"].set_index("time").index.tz_convert("US/Pacific")
|
|
|
|
# trying to set a single element on a part of a different timezone
|
|
# this converts to object
|
|
df2 = df.copy()
|
|
with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"):
|
|
df2.loc[df2.new_col == "new", "time"] = v
|
|
|
|
expected = Series([v[0], df.loc[1, "time"]], name="time")
|
|
tm.assert_series_equal(df2.time, expected)
|
|
|
|
v = df.loc[df.new_col == "new", "time"] + Timedelta("1s")
|
|
df.loc[df.new_col == "new", "time"] = v
|
|
tm.assert_series_equal(df.loc[df.new_col == "new", "time"], v)
|
|
|
|
def test_loc_setitem_with_expansion_inf_upcast_empty(self):
|
|
# Test with np.inf in columns
|
|
df = DataFrame()
|
|
df.loc[0, 0] = 1
|
|
df.loc[1, 1] = 2
|
|
df.loc[0, np.inf] = 3
|
|
|
|
result = df.columns
|
|
expected = Float64Index([0, 1, np.inf])
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
@pytest.mark.filterwarnings("ignore:indexing past lexsort depth")
|
|
def test_loc_setitem_with_expansion_nonunique_index(self, index, request):
|
|
# GH#40096
|
|
if not len(index):
|
|
return
|
|
|
|
index = index.repeat(2) # ensure non-unique
|
|
N = len(index)
|
|
arr = np.arange(N).astype(np.int64)
|
|
|
|
orig = DataFrame(arr, index=index, columns=[0])
|
|
|
|
# key that will requiring object-dtype casting in the index
|
|
key = "kapow"
|
|
assert key not in index # otherwise test is invalid
|
|
# TODO: using a tuple key breaks here in many cases
|
|
|
|
exp_index = index.insert(len(index), key)
|
|
if isinstance(index, MultiIndex):
|
|
assert exp_index[-1][0] == key
|
|
else:
|
|
assert exp_index[-1] == key
|
|
exp_data = np.arange(N + 1).astype(np.float64)
|
|
expected = DataFrame(exp_data, index=exp_index, columns=[0])
|
|
|
|
# Add new row, but no new columns
|
|
df = orig.copy()
|
|
df.loc[key, 0] = N
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# add new row on a Series
|
|
ser = orig.copy()[0]
|
|
ser.loc[key] = N
|
|
# the series machinery lets us preserve int dtype instead of float
|
|
expected = expected[0].astype(np.int64)
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
# add new row and new column
|
|
df = orig.copy()
|
|
df.loc[key, 1] = N
|
|
expected = DataFrame(
|
|
{0: list(arr) + [np.nan], 1: [np.nan] * N + [float(N)]},
|
|
index=exp_index,
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dtype", ["Int32", "Int64", "UInt32", "UInt64", "Float32", "Float64"]
|
|
)
|
|
def test_loc_setitem_with_expansion_preserves_nullable_int(self, dtype):
|
|
# GH#42099
|
|
ser = Series([0, 1, 2, 3], dtype=dtype)
|
|
df = DataFrame({"data": ser})
|
|
|
|
result = DataFrame(index=df.index)
|
|
result.loc[df.index, "data"] = ser
|
|
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = DataFrame(index=df.index)
|
|
result.loc[df.index, "data"] = ser._values
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
|
|
class TestLocCallable:
|
|
def test_frame_loc_getitem_callable(self):
|
|
# GH#11485
|
|
df = DataFrame({"A": [1, 2, 3, 4], "B": list("aabb"), "C": [1, 2, 3, 4]})
|
|
# iloc cannot use boolean Series (see GH3635)
|
|
|
|
# return bool indexer
|
|
res = df.loc[lambda x: x.A > 2]
|
|
tm.assert_frame_equal(res, df.loc[df.A > 2])
|
|
|
|
res = df.loc[lambda x: x.B == "b", :]
|
|
tm.assert_frame_equal(res, df.loc[df.B == "b", :])
|
|
|
|
res = df.loc[lambda x: x.A > 2, lambda x: x.columns == "B"]
|
|
tm.assert_frame_equal(res, df.loc[df.A > 2, [False, True, False]])
|
|
|
|
res = df.loc[lambda x: x.A > 2, lambda x: "B"]
|
|
tm.assert_series_equal(res, df.loc[df.A > 2, "B"])
|
|
|
|
res = df.loc[lambda x: x.A > 2, lambda x: ["A", "B"]]
|
|
tm.assert_frame_equal(res, df.loc[df.A > 2, ["A", "B"]])
|
|
|
|
res = df.loc[lambda x: x.A == 2, lambda x: ["A", "B"]]
|
|
tm.assert_frame_equal(res, df.loc[df.A == 2, ["A", "B"]])
|
|
|
|
# scalar
|
|
res = df.loc[lambda x: 1, lambda x: "A"]
|
|
assert res == df.loc[1, "A"]
|
|
|
|
def test_frame_loc_getitem_callable_mixture(self):
|
|
# GH#11485
|
|
df = DataFrame({"A": [1, 2, 3, 4], "B": list("aabb"), "C": [1, 2, 3, 4]})
|
|
|
|
res = df.loc[lambda x: x.A > 2, ["A", "B"]]
|
|
tm.assert_frame_equal(res, df.loc[df.A > 2, ["A", "B"]])
|
|
|
|
res = df.loc[[2, 3], lambda x: ["A", "B"]]
|
|
tm.assert_frame_equal(res, df.loc[[2, 3], ["A", "B"]])
|
|
|
|
res = df.loc[3, lambda x: ["A", "B"]]
|
|
tm.assert_series_equal(res, df.loc[3, ["A", "B"]])
|
|
|
|
def test_frame_loc_getitem_callable_labels(self):
|
|
# GH#11485
|
|
df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD"))
|
|
|
|
# return label
|
|
res = df.loc[lambda x: ["A", "C"]]
|
|
tm.assert_frame_equal(res, df.loc[["A", "C"]])
|
|
|
|
res = df.loc[lambda x: ["A", "C"], :]
|
|
tm.assert_frame_equal(res, df.loc[["A", "C"], :])
|
|
|
|
res = df.loc[lambda x: ["A", "C"], lambda x: "X"]
|
|
tm.assert_series_equal(res, df.loc[["A", "C"], "X"])
|
|
|
|
res = df.loc[lambda x: ["A", "C"], lambda x: ["X"]]
|
|
tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]])
|
|
|
|
# mixture
|
|
res = df.loc[["A", "C"], lambda x: "X"]
|
|
tm.assert_series_equal(res, df.loc[["A", "C"], "X"])
|
|
|
|
res = df.loc[["A", "C"], lambda x: ["X"]]
|
|
tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]])
|
|
|
|
res = df.loc[lambda x: ["A", "C"], "X"]
|
|
tm.assert_series_equal(res, df.loc[["A", "C"], "X"])
|
|
|
|
res = df.loc[lambda x: ["A", "C"], ["X"]]
|
|
tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]])
|
|
|
|
def test_frame_loc_setitem_callable(self):
|
|
# GH#11485
|
|
df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD"))
|
|
|
|
# return label
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"]] = -20
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"]] = -20
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"], :] = 20
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], :] = 20
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"], lambda x: "X"] = -1
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], "X"] = -1
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"], lambda x: ["X"]] = [5, 10]
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], ["X"]] = [5, 10]
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
# mixture
|
|
res = df.copy()
|
|
res.loc[["A", "C"], lambda x: "X"] = np.array([-1, -2])
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], "X"] = np.array([-1, -2])
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[["A", "C"], lambda x: ["X"]] = 10
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], ["X"]] = 10
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"], "X"] = -2
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], "X"] = -2
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.copy()
|
|
res.loc[lambda x: ["A", "C"], ["X"]] = -4
|
|
exp = df.copy()
|
|
exp.loc[["A", "C"], ["X"]] = -4
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
|
|
class TestPartialStringSlicing:
|
|
def test_loc_getitem_partial_string_slicing_datetimeindex(self):
|
|
# GH#35509
|
|
df = DataFrame(
|
|
{"col1": ["a", "b", "c"], "col2": [1, 2, 3]},
|
|
index=to_datetime(["2020-08-01", "2020-07-02", "2020-08-05"]),
|
|
)
|
|
expected = DataFrame(
|
|
{"col1": ["a", "c"], "col2": [1, 3]},
|
|
index=to_datetime(["2020-08-01", "2020-08-05"]),
|
|
)
|
|
result = df.loc["2020-08"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_partial_string_slicing_with_periodindex(self):
|
|
pi = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M")
|
|
ser = pi.to_series()
|
|
result = ser.loc[:"2017-12"]
|
|
expected = ser.iloc[:-1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_getitem_partial_string_slicing_with_timedeltaindex(self):
|
|
ix = timedelta_range(start="1 day", end="2 days", freq="1H")
|
|
ser = ix.to_series()
|
|
result = ser.loc[:"1 days"]
|
|
expected = ser.iloc[:-1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_getitem_str_timedeltaindex(self):
|
|
# GH#16896
|
|
df = DataFrame({"x": range(3)}, index=to_timedelta(range(3), unit="days"))
|
|
expected = df.iloc[0]
|
|
sliced = df.loc["0 days"]
|
|
tm.assert_series_equal(sliced, expected)
|
|
|
|
@pytest.mark.parametrize("indexer_end", [None, "2020-01-02 23:59:59.999999999"])
|
|
def test_loc_getitem_partial_slice_non_monotonicity(
|
|
self, tz_aware_fixture, indexer_end, frame_or_series
|
|
):
|
|
# GH#33146
|
|
obj = frame_or_series(
|
|
[1] * 5,
|
|
index=DatetimeIndex(
|
|
[
|
|
Timestamp("2019-12-30"),
|
|
Timestamp("2020-01-01"),
|
|
Timestamp("2019-12-25"),
|
|
Timestamp("2020-01-02 23:59:59.999999999"),
|
|
Timestamp("2019-12-19"),
|
|
],
|
|
tz=tz_aware_fixture,
|
|
),
|
|
)
|
|
expected = frame_or_series(
|
|
[1] * 2,
|
|
index=DatetimeIndex(
|
|
[
|
|
Timestamp("2020-01-01"),
|
|
Timestamp("2020-01-02 23:59:59.999999999"),
|
|
],
|
|
tz=tz_aware_fixture,
|
|
),
|
|
)
|
|
indexer = slice("2020-01-01", indexer_end)
|
|
|
|
result = obj[indexer]
|
|
tm.assert_equal(result, expected)
|
|
|
|
result = obj.loc[indexer]
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
class TestLabelSlicing:
|
|
def test_loc_getitem_slicing_datetimes_frame(self):
|
|
# GH#7523
|
|
|
|
# unique
|
|
df_unique = DataFrame(
|
|
np.arange(4.0, dtype="float64"),
|
|
index=[datetime(2001, 1, i, 10, 00) for i in [1, 2, 3, 4]],
|
|
)
|
|
|
|
# duplicates
|
|
df_dups = DataFrame(
|
|
np.arange(5.0, dtype="float64"),
|
|
index=[datetime(2001, 1, i, 10, 00) for i in [1, 2, 2, 3, 4]],
|
|
)
|
|
|
|
for df in [df_unique, df_dups]:
|
|
result = df.loc[datetime(2001, 1, 1, 10) :]
|
|
tm.assert_frame_equal(result, df)
|
|
result = df.loc[: datetime(2001, 1, 4, 10)]
|
|
tm.assert_frame_equal(result, df)
|
|
result = df.loc[datetime(2001, 1, 1, 10) : datetime(2001, 1, 4, 10)]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.loc[datetime(2001, 1, 1, 11) :]
|
|
expected = df.iloc[1:]
|
|
tm.assert_frame_equal(result, expected)
|
|
result = df.loc["20010101 11":]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_loc_getitem_label_slice_across_dst(self):
|
|
# GH#21846
|
|
idx = date_range(
|
|
"2017-10-29 01:30:00", tz="Europe/Berlin", periods=5, freq="30 min"
|
|
)
|
|
series2 = Series([0, 1, 2, 3, 4], index=idx)
|
|
|
|
t_1 = Timestamp("2017-10-29 02:30:00+02:00", tz="Europe/Berlin")
|
|
t_2 = Timestamp("2017-10-29 02:00:00+01:00", tz="Europe/Berlin")
|
|
result = series2.loc[t_1:t_2]
|
|
expected = Series([2, 3], index=idx[2:4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = series2[t_1]
|
|
expected = 2
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"index",
|
|
[
|
|
pd.period_range(start="2017-01-01", end="2018-01-01", freq="M"),
|
|
timedelta_range(start="1 day", end="2 days", freq="1H"),
|
|
],
|
|
)
|
|
def test_loc_getitem_label_slice_period_timedelta(self, index):
|
|
ser = index.to_series()
|
|
result = ser.loc[: index[-2]]
|
|
expected = ser.iloc[:-1]
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_loc_getitem_slice_floats_inexact(self):
|
|
index = [52195.504153, 52196.303147, 52198.369883]
|
|
df = DataFrame(np.random.rand(3, 2), index=index)
|
|
|
|
s1 = df.loc[52195.1:52196.5]
|
|
assert len(s1) == 2
|
|
|
|
s1 = df.loc[52195.1:52196.6]
|
|
assert len(s1) == 2
|
|
|
|
s1 = df.loc[52195.1:52198.9]
|
|
assert len(s1) == 3
|
|
|
|
def test_loc_getitem_float_slice_float64index(self):
|
|
ser = Series(np.random.rand(10), index=np.arange(10, 20, dtype=float))
|
|
|
|
assert len(ser.loc[12.0:]) == 8
|
|
assert len(ser.loc[12.5:]) == 7
|
|
|
|
idx = np.arange(10, 20, dtype=float)
|
|
idx[2] = 12.2
|
|
ser.index = idx
|
|
assert len(ser.loc[12.0:]) == 8
|
|
assert len(ser.loc[12.5:]) == 7
|
|
|
|
@pytest.mark.parametrize(
|
|
"start,stop, expected_slice",
|
|
[
|
|
[np.timedelta64(0, "ns"), None, slice(0, 11)],
|
|
[np.timedelta64(1, "D"), np.timedelta64(6, "D"), slice(1, 7)],
|
|
[None, np.timedelta64(4, "D"), slice(0, 5)],
|
|
],
|
|
)
|
|
def test_loc_getitem_slice_label_td64obj(self, start, stop, expected_slice):
|
|
# GH#20393
|
|
ser = Series(range(11), timedelta_range("0 days", "10 days"))
|
|
result = ser.loc[slice(start, stop)]
|
|
expected = ser.iloc[expected_slice]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("start", ["2018", "2020"])
|
|
def test_loc_getitem_slice_unordered_dt_index(self, frame_or_series, start):
|
|
obj = frame_or_series(
|
|
[1, 2, 3],
|
|
index=[Timestamp("2016"), Timestamp("2019"), Timestamp("2017")],
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
obj.loc[start:"2022"]
|
|
|
|
@pytest.mark.parametrize("value", [1, 1.5])
|
|
def test_loc_getitem_slice_labels_int_in_object_index(self, frame_or_series, value):
|
|
# GH: 26491
|
|
obj = frame_or_series(range(4), index=[value, "first", 2, "third"])
|
|
result = obj.loc[value:"third"]
|
|
expected = frame_or_series(range(4), index=[value, "first", 2, "third"])
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_loc_getitem_slice_columns_mixed_dtype(self):
|
|
# GH: 20975
|
|
df = DataFrame({"test": 1, 1: 2, 2: 3}, index=[0])
|
|
expected = DataFrame(
|
|
data=[[2, 3]], index=[0], columns=Index([1, 2], dtype=object)
|
|
)
|
|
tm.assert_frame_equal(df.loc[:, 1:], expected)
|
|
|
|
|
|
class TestLocBooleanLabelsAndSlices(Base):
|
|
@pytest.mark.parametrize("bool_value", [True, False])
|
|
def test_loc_bool_incompatible_index_raises(
|
|
self, index, frame_or_series, bool_value
|
|
):
|
|
# GH20432
|
|
message = f"{bool_value}: boolean label can not be used without a boolean index"
|
|
if index.inferred_type != "boolean":
|
|
obj = frame_or_series(index=index, dtype="object")
|
|
with pytest.raises(KeyError, match=message):
|
|
obj.loc[bool_value]
|
|
|
|
@pytest.mark.parametrize("bool_value", [True, False])
|
|
def test_loc_bool_should_not_raise(self, frame_or_series, bool_value):
|
|
obj = frame_or_series(
|
|
index=Index([True, False], dtype="boolean"), dtype="object"
|
|
)
|
|
obj.loc[bool_value]
|
|
|
|
def test_loc_bool_slice_raises(self, index, frame_or_series):
|
|
# GH20432
|
|
message = (
|
|
r"slice\(True, False, None\): boolean values can not be used in a slice"
|
|
)
|
|
obj = frame_or_series(index=index, dtype="object")
|
|
with pytest.raises(TypeError, match=message):
|
|
obj.loc[True:False]
|
|
|
|
|
|
class TestLocBooleanMask:
|
|
def test_loc_setitem_bool_mask_timedeltaindex(self):
|
|
# GH#14946
|
|
df = DataFrame({"x": range(10)})
|
|
df.index = to_timedelta(range(10), unit="s")
|
|
conditions = [df["x"] > 3, df["x"] == 3, df["x"] < 3]
|
|
expected_data = [
|
|
[0, 1, 2, 3, 10, 10, 10, 10, 10, 10],
|
|
[0, 1, 2, 10, 4, 5, 6, 7, 8, 9],
|
|
[10, 10, 10, 3, 4, 5, 6, 7, 8, 9],
|
|
]
|
|
for cond, data in zip(conditions, expected_data):
|
|
result = df.copy()
|
|
result.loc[cond, "x"] = 10
|
|
|
|
expected = DataFrame(
|
|
data,
|
|
index=to_timedelta(range(10), unit="s"),
|
|
columns=["x"],
|
|
dtype="int64",
|
|
)
|
|
tm.assert_frame_equal(expected, result)
|
|
|
|
@pytest.mark.parametrize("tz", [None, "UTC"])
|
|
def test_loc_setitem_mask_with_datetimeindex_tz(self, tz):
|
|
# GH#16889
|
|
# support .loc with alignment and tz-aware DatetimeIndex
|
|
mask = np.array([True, False, True, False])
|
|
|
|
idx = date_range("20010101", periods=4, tz=tz)
|
|
df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64")
|
|
|
|
result = df.copy()
|
|
result.loc[mask, :] = df.loc[mask, :]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
result = df.copy()
|
|
result.loc[mask] = df.loc[mask]
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_loc_setitem_mask_and_label_with_datetimeindex(self):
|
|
# GH#9478
|
|
# a datetimeindex alignment issue with partial setting
|
|
df = DataFrame(
|
|
np.arange(6.0).reshape(3, 2),
|
|
columns=list("AB"),
|
|
index=date_range("1/1/2000", periods=3, freq="1H"),
|
|
)
|
|
expected = df.copy()
|
|
expected["C"] = [expected.index[0]] + [pd.NaT, pd.NaT]
|
|
|
|
mask = df.A < 1
|
|
df.loc[mask, "C"] = df.loc[mask].index
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_setitem_mask_td64_series_value(self):
|
|
# GH#23462 key list of bools, value is a Series
|
|
td1 = Timedelta(0)
|
|
td2 = Timedelta(28767471428571405)
|
|
df = DataFrame({"col": Series([td1, td2])})
|
|
df_copy = df.copy()
|
|
ser = Series([td1])
|
|
|
|
expected = df["col"].iloc[1].value
|
|
df.loc[[True, False]] = ser
|
|
result = df["col"].iloc[1].value
|
|
|
|
assert expected == result
|
|
tm.assert_frame_equal(df, df_copy)
|
|
|
|
@td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values
|
|
def test_loc_setitem_boolean_and_column(self, float_frame):
|
|
expected = float_frame.copy()
|
|
mask = float_frame["A"] > 0
|
|
|
|
float_frame.loc[mask, "B"] = 0
|
|
expected.values[mask.values, 1] = 0
|
|
|
|
tm.assert_frame_equal(float_frame, expected)
|
|
|
|
|
|
class TestLocListlike:
|
|
@pytest.mark.parametrize("box", [lambda x: x, np.asarray, list])
|
|
def test_loc_getitem_list_of_labels_categoricalindex_with_na(self, box):
|
|
# passing a list can include valid categories _or_ NA values
|
|
ci = CategoricalIndex(["A", "B", np.nan])
|
|
ser = Series(range(3), index=ci)
|
|
|
|
result = ser.loc[box(ci)]
|
|
tm.assert_series_equal(result, ser)
|
|
|
|
result = ser[box(ci)]
|
|
tm.assert_series_equal(result, ser)
|
|
|
|
result = ser.to_frame().loc[box(ci)]
|
|
tm.assert_frame_equal(result, ser.to_frame())
|
|
|
|
ser2 = ser[:-1]
|
|
ci2 = ci[1:]
|
|
# but if there are no NAs present, this should raise KeyError
|
|
msg = "not in index"
|
|
with pytest.raises(KeyError, match=msg):
|
|
ser2.loc[box(ci2)]
|
|
|
|
with pytest.raises(KeyError, match=msg):
|
|
ser2[box(ci2)]
|
|
|
|
with pytest.raises(KeyError, match=msg):
|
|
ser2.to_frame().loc[box(ci2)]
|
|
|
|
def test_loc_getitem_series_label_list_missing_values(self):
|
|
# gh-11428
|
|
key = np.array(
|
|
["2001-01-04", "2001-01-02", "2001-01-04", "2001-01-14"], dtype="datetime64"
|
|
)
|
|
ser = Series([2, 5, 8, 11], date_range("2001-01-01", freq="D", periods=4))
|
|
with pytest.raises(KeyError, match="not in index"):
|
|
ser.loc[key]
|
|
|
|
def test_loc_getitem_series_label_list_missing_integer_values(self):
|
|
# GH: 25927
|
|
ser = Series(
|
|
index=np.array([9730701000001104, 10049011000001109]),
|
|
data=np.array([999000011000001104, 999000011000001104]),
|
|
)
|
|
with pytest.raises(KeyError, match="not in index"):
|
|
ser.loc[np.array([9730701000001104, 10047311000001102])]
|
|
|
|
@pytest.mark.parametrize("to_period", [True, False])
|
|
def test_loc_getitem_listlike_of_datetimelike_keys(self, to_period):
|
|
# GH#11497
|
|
|
|
idx = date_range("2011-01-01", "2011-01-02", freq="D", name="idx")
|
|
if to_period:
|
|
idx = idx.to_period("D")
|
|
ser = Series([0.1, 0.2], index=idx, name="s")
|
|
|
|
keys = [Timestamp("2011-01-01"), Timestamp("2011-01-02")]
|
|
if to_period:
|
|
keys = [x.to_period("D") for x in keys]
|
|
result = ser.loc[keys]
|
|
exp = Series([0.1, 0.2], index=idx, name="s")
|
|
if not to_period:
|
|
exp.index = exp.index._with_freq(None)
|
|
tm.assert_series_equal(result, exp, check_index_type=True)
|
|
|
|
keys = [
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-01"),
|
|
]
|
|
if to_period:
|
|
keys = [x.to_period("D") for x in keys]
|
|
exp = Series(
|
|
[0.2, 0.2, 0.1], index=Index(keys, name="idx", dtype=idx.dtype), name="s"
|
|
)
|
|
result = ser.loc[keys]
|
|
tm.assert_series_equal(result, exp, check_index_type=True)
|
|
|
|
keys = [
|
|
Timestamp("2011-01-03"),
|
|
Timestamp("2011-01-02"),
|
|
Timestamp("2011-01-03"),
|
|
]
|
|
if to_period:
|
|
keys = [x.to_period("D") for x in keys]
|
|
|
|
with pytest.raises(KeyError, match="not in index"):
|
|
ser.loc[keys]
|
|
|
|
def test_loc_named_index(self):
|
|
# GH 42790
|
|
df = DataFrame(
|
|
[[1, 2], [4, 5], [7, 8]],
|
|
index=["cobra", "viper", "sidewinder"],
|
|
columns=["max_speed", "shield"],
|
|
)
|
|
expected = df.iloc[:2]
|
|
expected.index.name = "foo"
|
|
result = df.loc[Index(["cobra", "viper"], name="foo")]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"columns, column_key, expected_columns",
|
|
[
|
|
([2011, 2012, 2013], [2011, 2012], [0, 1]),
|
|
([2011, 2012, "All"], [2011, 2012], [0, 1]),
|
|
([2011, 2012, "All"], [2011, "All"], [0, 2]),
|
|
],
|
|
)
|
|
def test_loc_getitem_label_list_integer_labels(columns, column_key, expected_columns):
|
|
# gh-14836
|
|
df = DataFrame(np.random.rand(3, 3), columns=columns, index=list("ABC"))
|
|
expected = df.iloc[:, expected_columns]
|
|
result = df.loc[["A", "B", "C"], column_key]
|
|
|
|
tm.assert_frame_equal(result, expected, check_column_type=True)
|
|
|
|
|
|
def test_loc_setitem_float_intindex():
|
|
# GH 8720
|
|
rand_data = np.random.randn(8, 4)
|
|
result = DataFrame(rand_data)
|
|
result.loc[:, 0.5] = np.nan
|
|
expected_data = np.hstack((rand_data, np.array([np.nan] * 8).reshape(8, 1)))
|
|
expected = DataFrame(expected_data, columns=[0.0, 1.0, 2.0, 3.0, 0.5])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = DataFrame(rand_data)
|
|
result.loc[:, 0.5] = np.nan
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_loc_axis_1_slice():
|
|
# GH 10586
|
|
cols = [(yr, m) for yr in [2014, 2015] for m in [7, 8, 9, 10]]
|
|
df = DataFrame(
|
|
np.ones((10, 8)),
|
|
index=tuple("ABCDEFGHIJ"),
|
|
columns=MultiIndex.from_tuples(cols),
|
|
)
|
|
result = df.loc(axis=1)[(2014, 9):(2015, 8)]
|
|
expected = DataFrame(
|
|
np.ones((10, 4)),
|
|
index=tuple("ABCDEFGHIJ"),
|
|
columns=MultiIndex.from_tuples([(2014, 9), (2014, 10), (2015, 7), (2015, 8)]),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_loc_set_dataframe_multiindex():
|
|
# GH 14592
|
|
expected = DataFrame(
|
|
"a", index=range(2), columns=MultiIndex.from_product([range(2), range(2)])
|
|
)
|
|
result = expected.copy()
|
|
result.loc[0, [(0, 1)]] = result.loc[0, [(0, 1)]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_loc_mixed_int_float():
|
|
# GH#19456
|
|
ser = Series(range(2), Index([1, 2.0], dtype=object))
|
|
|
|
result = ser.loc[1]
|
|
assert result == 0
|
|
|
|
|
|
def test_loc_with_positional_slice_deprecation():
|
|
# GH#31840
|
|
ser = Series(range(4), index=["A", "B", "C", "D"])
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
ser.loc[:3] = 2
|
|
|
|
expected = Series([2, 2, 2, 3], index=["A", "B", "C", "D"])
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
|
|
def test_loc_slice_disallows_positional():
|
|
# GH#16121, GH#24612, GH#31810
|
|
dti = date_range("2016-01-01", periods=3)
|
|
df = DataFrame(np.random.random((3, 2)), index=dti)
|
|
|
|
ser = df[0]
|
|
|
|
msg = (
|
|
"cannot do slice indexing on DatetimeIndex with these "
|
|
r"indexers \[1\] of type int"
|
|
)
|
|
|
|
for obj in [df, ser]:
|
|
with pytest.raises(TypeError, match=msg):
|
|
obj.loc[1:3]
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
# GH#31840 deprecated incorrect behavior
|
|
obj.loc[1:3] = 1
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.loc[1:3, 1]
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
# GH#31840 deprecated incorrect behavior
|
|
df.loc[1:3, 1] = 2
|
|
|
|
|
|
def test_loc_datetimelike_mismatched_dtypes():
|
|
# GH#32650 dont mix and match datetime/timedelta/period dtypes
|
|
|
|
df = DataFrame(
|
|
np.random.randn(5, 3),
|
|
columns=["a", "b", "c"],
|
|
index=date_range("2012", freq="H", periods=5),
|
|
)
|
|
# create dataframe with non-unique DatetimeIndex
|
|
df = df.iloc[[0, 2, 2, 3]].copy()
|
|
|
|
dti = df.index
|
|
tdi = pd.TimedeltaIndex(dti.asi8) # matching i8 values
|
|
|
|
msg = r"None of \[TimedeltaIndex.* are in the \[index\]"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[tdi]
|
|
|
|
with pytest.raises(KeyError, match=msg):
|
|
df["a"].loc[tdi]
|
|
|
|
|
|
def test_loc_with_period_index_indexer():
|
|
# GH#4125
|
|
idx = pd.period_range("2002-01", "2003-12", freq="M")
|
|
df = DataFrame(np.random.randn(24, 10), index=idx)
|
|
tm.assert_frame_equal(df, df.loc[idx])
|
|
tm.assert_frame_equal(df, df.loc[list(idx)])
|
|
tm.assert_frame_equal(df, df.loc[list(idx)])
|
|
tm.assert_frame_equal(df.iloc[0:5], df.loc[idx[0:5]])
|
|
tm.assert_frame_equal(df, df.loc[list(idx)])
|
|
|
|
|
|
def test_loc_getitem_multiindex_tuple_level():
|
|
# GH#27591
|
|
lev1 = ["a", "b", "c"]
|
|
lev2 = [(0, 1), (1, 0)]
|
|
lev3 = [0, 1]
|
|
cols = MultiIndex.from_product([lev1, lev2, lev3], names=["x", "y", "z"])
|
|
df = DataFrame(6, index=range(5), columns=cols)
|
|
|
|
# the lev2[0] here should be treated as a single label, not as a sequence
|
|
# of labels
|
|
result = df.loc[:, (lev1[0], lev2[0], lev3[0])]
|
|
|
|
# TODO: i think this actually should drop levels
|
|
expected = df.iloc[:, :1]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
alt = df.xs((lev1[0], lev2[0], lev3[0]), level=[0, 1, 2], axis=1)
|
|
tm.assert_frame_equal(alt, expected)
|
|
|
|
# same thing on a Series
|
|
ser = df.iloc[0]
|
|
expected2 = ser.iloc[:1]
|
|
|
|
alt2 = ser.xs((lev1[0], lev2[0], lev3[0]), level=[0, 1, 2], axis=0)
|
|
tm.assert_series_equal(alt2, expected2)
|
|
|
|
result2 = ser.loc[lev1[0], lev2[0], lev3[0]]
|
|
assert result2 == 6
|
|
|
|
|
|
def test_loc_getitem_nullable_index_with_duplicates():
|
|
# GH#34497
|
|
df = DataFrame(
|
|
data=np.array([[1, 2, 3, 4], [5, 6, 7, 8], [1, 2, np.nan, np.nan]]).T,
|
|
columns=["a", "b", "c"],
|
|
dtype="Int64",
|
|
)
|
|
df2 = df.set_index("c")
|
|
assert df2.index.dtype == "Int64"
|
|
|
|
res = df2.loc[1]
|
|
expected = Series([1, 5], index=df2.columns, dtype="Int64", name=1)
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
# pd.NA and duplicates in an object-dtype Index
|
|
df2.index = df2.index.astype(object)
|
|
res = df2.loc[1]
|
|
tm.assert_series_equal(res, expected)
|
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class TestLocSeries:
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@pytest.mark.parametrize("val,expected", [(2**63 - 1, 3), (2**63, 4)])
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def test_loc_uint64(self, val, expected):
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# see GH#19399
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ser = Series({2**63 - 1: 3, 2**63: 4})
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assert ser.loc[val] == expected
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def test_loc_getitem(self, string_series, datetime_series):
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inds = string_series.index[[3, 4, 7]]
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tm.assert_series_equal(string_series.loc[inds], string_series.reindex(inds))
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tm.assert_series_equal(string_series.iloc[5::2], string_series[5::2])
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# slice with indices
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d1, d2 = datetime_series.index[[5, 15]]
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result = datetime_series.loc[d1:d2]
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expected = datetime_series.truncate(d1, d2)
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tm.assert_series_equal(result, expected)
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# boolean
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mask = string_series > string_series.median()
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tm.assert_series_equal(string_series.loc[mask], string_series[mask])
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# ask for index value
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assert datetime_series.loc[d1] == datetime_series[d1]
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assert datetime_series.loc[d2] == datetime_series[d2]
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def test_loc_getitem_not_monotonic(self, datetime_series):
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d1, d2 = datetime_series.index[[5, 15]]
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ts2 = datetime_series[::2][[1, 2, 0]]
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msg = r"Timestamp\('2000-01-10 00:00:00'\)"
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with pytest.raises(KeyError, match=msg):
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ts2.loc[d1:d2]
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with pytest.raises(KeyError, match=msg):
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ts2.loc[d1:d2] = 0
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def test_loc_getitem_setitem_integer_slice_keyerrors(self):
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ser = Series(np.random.randn(10), index=list(range(0, 20, 2)))
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# this is OK
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cp = ser.copy()
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cp.iloc[4:10] = 0
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assert (cp.iloc[4:10] == 0).all()
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# so is this
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cp = ser.copy()
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cp.iloc[3:11] = 0
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assert (cp.iloc[3:11] == 0).values.all()
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result = ser.iloc[2:6]
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result2 = ser.loc[3:11]
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expected = ser.reindex([4, 6, 8, 10])
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tm.assert_series_equal(result, expected)
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tm.assert_series_equal(result2, expected)
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# non-monotonic, raise KeyError
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s2 = ser.iloc[list(range(5)) + list(range(9, 4, -1))]
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with pytest.raises(KeyError, match=r"^3$"):
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s2.loc[3:11]
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with pytest.raises(KeyError, match=r"^3$"):
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s2.loc[3:11] = 0
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def test_loc_getitem_iterator(self, string_series):
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idx = iter(string_series.index[:10])
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result = string_series.loc[idx]
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tm.assert_series_equal(result, string_series[:10])
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def test_loc_setitem_boolean(self, string_series):
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mask = string_series > string_series.median()
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result = string_series.copy()
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result.loc[mask] = 0
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expected = string_series
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expected[mask] = 0
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tm.assert_series_equal(result, expected)
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def test_loc_setitem_corner(self, string_series):
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inds = list(string_series.index[[5, 8, 12]])
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string_series.loc[inds] = 5
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msg = r"\['foo'\] not in index"
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with pytest.raises(KeyError, match=msg):
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string_series.loc[inds + ["foo"]] = 5
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def test_basic_setitem_with_labels(self, datetime_series):
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indices = datetime_series.index[[5, 10, 15]]
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cp = datetime_series.copy()
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exp = datetime_series.copy()
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cp[indices] = 0
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exp.loc[indices] = 0
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tm.assert_series_equal(cp, exp)
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cp = datetime_series.copy()
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exp = datetime_series.copy()
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cp[indices[0] : indices[2]] = 0
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exp.loc[indices[0] : indices[2]] = 0
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tm.assert_series_equal(cp, exp)
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def test_loc_setitem_listlike_of_ints(self):
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# integer indexes, be careful
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ser = Series(np.random.randn(10), index=list(range(0, 20, 2)))
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inds = [0, 4, 6]
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arr_inds = np.array([0, 4, 6])
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cp = ser.copy()
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exp = ser.copy()
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ser[inds] = 0
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ser.loc[inds] = 0
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tm.assert_series_equal(cp, exp)
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cp = ser.copy()
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exp = ser.copy()
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ser[arr_inds] = 0
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ser.loc[arr_inds] = 0
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tm.assert_series_equal(cp, exp)
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inds_notfound = [0, 4, 5, 6]
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arr_inds_notfound = np.array([0, 4, 5, 6])
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msg = r"\[5\] not in index"
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with pytest.raises(KeyError, match=msg):
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ser[inds_notfound] = 0
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with pytest.raises(Exception, match=msg):
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ser[arr_inds_notfound] = 0
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def test_loc_setitem_dt64tz_values(self):
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# GH#12089
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ser = Series(
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date_range("2011-01-01", periods=3, tz="US/Eastern"),
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index=["a", "b", "c"],
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)
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s2 = ser.copy()
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expected = Timestamp("2011-01-03", tz="US/Eastern")
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s2.loc["a"] = expected
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result = s2.loc["a"]
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assert result == expected
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s2 = ser.copy()
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s2.iloc[0] = expected
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result = s2.iloc[0]
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assert result == expected
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s2 = ser.copy()
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s2["a"] = expected
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result = s2["a"]
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assert result == expected
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@pytest.mark.parametrize("array_fn", [np.array, pd.array, list, tuple])
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@pytest.mark.parametrize("size", [0, 4, 5, 6])
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def test_loc_iloc_setitem_with_listlike(self, size, array_fn):
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# GH37748
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# testing insertion, in a Series of size N (here 5), of a listlike object
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# of size 0, N-1, N, N+1
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arr = array_fn([0] * size)
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expected = Series([arr, 0, 0, 0, 0], index=list("abcde"), dtype=object)
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ser = Series(0, index=list("abcde"), dtype=object)
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ser.loc["a"] = arr
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tm.assert_series_equal(ser, expected)
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ser = Series(0, index=list("abcde"), dtype=object)
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ser.iloc[0] = arr
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tm.assert_series_equal(ser, expected)
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@pytest.mark.parametrize("indexer", [IndexSlice["A", :], ("A", slice(None))])
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def test_loc_series_getitem_too_many_dimensions(self, indexer):
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# GH#35349
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ser = Series(
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index=MultiIndex.from_tuples([("A", "0"), ("A", "1"), ("B", "0")]),
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data=[21, 22, 23],
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)
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msg = "Too many indices"
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with pytest.raises(ValueError, match=msg):
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ser.loc[indexer, :]
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with pytest.raises(ValueError, match=msg):
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ser.loc[indexer, :] = 1
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def test_loc_setitem(self, string_series):
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inds = string_series.index[[3, 4, 7]]
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result = string_series.copy()
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result.loc[inds] = 5
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expected = string_series.copy()
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expected[[3, 4, 7]] = 5
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tm.assert_series_equal(result, expected)
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result.iloc[5:10] = 10
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expected[5:10] = 10
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tm.assert_series_equal(result, expected)
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# set slice with indices
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d1, d2 = string_series.index[[5, 15]]
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result.loc[d1:d2] = 6
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expected[5:16] = 6 # because it's inclusive
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tm.assert_series_equal(result, expected)
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# set index value
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string_series.loc[d1] = 4
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string_series.loc[d2] = 6
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assert string_series[d1] == 4
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assert string_series[d2] == 6
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@pytest.mark.parametrize("dtype", ["object", "string"])
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def test_loc_assign_dict_to_row(self, dtype):
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# GH41044
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df = DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype=dtype)
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df.loc[0, :] = {"A": "newA", "B": "newB"}
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expected = DataFrame({"A": ["newA", "def"], "B": ["newB", "jkl"]}, dtype=dtype)
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tm.assert_frame_equal(df, expected)
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@td.skip_array_manager_invalid_test
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def test_loc_setitem_dict_timedelta_multiple_set(self):
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# GH 16309
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result = DataFrame(columns=["time", "value"])
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result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"}
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result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"}
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expected = DataFrame(
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[[Timedelta(6, unit="s"), "foo"]], columns=["time", "value"], index=[1]
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)
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tm.assert_frame_equal(result, expected)
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def test_loc_set_multiple_items_in_multiple_new_columns(self):
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# GH 25594
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df = DataFrame(index=[1, 2], columns=["a"])
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df.loc[1, ["b", "c"]] = [6, 7]
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expected = DataFrame(
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{
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"a": Series([np.nan, np.nan], dtype="object"),
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"b": [6, np.nan],
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"c": [7, np.nan],
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},
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index=[1, 2],
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)
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tm.assert_frame_equal(df, expected)
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def test_getitem_loc_str_periodindex(self):
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# GH#33964
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index = pd.period_range(start="2000", periods=20, freq="B")
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series = Series(range(20), index=index)
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assert series.loc["2000-01-14"] == 9
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