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3RNN/Lib/site-packages/pandas/tests/indexing/test_loc.py
s464967 8af59a8246 1.0
2024-05-26 19:49:15 +02:00

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""" test label based indexing with loc """
from collections import namedtuple
from datetime import (
date,
datetime,
time,
timedelta,
)
import re
from dateutil.tz import gettz
import numpy as np
import pytest
from pandas._config import using_pyarrow_string_dtype
from pandas._libs import index as libindex
from pandas.compat.numpy import np_version_gt2
from pandas.errors import IndexingError
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical,
CategoricalDtype,
CategoricalIndex,
DataFrame,
DatetimeIndex,
Index,
IndexSlice,
MultiIndex,
Period,
PeriodIndex,
Series,
SparseDtype,
Timedelta,
Timestamp,
date_range,
timedelta_range,
to_datetime,
to_timedelta,
)
import pandas._testing as tm
from pandas.api.types import is_scalar
from pandas.core.indexing import _one_ellipsis_message
from pandas.tests.indexing.common import check_indexing_smoketest_or_raises
@pytest.mark.parametrize(
"series, new_series, expected_ser",
[
[[np.nan, np.nan, "b"], ["a", np.nan, np.nan], [False, True, True]],
[[np.nan, "b"], ["a", np.nan], [False, True]],
],
)
def test_not_change_nan_loc(series, new_series, expected_ser):
# GH 28403
df = DataFrame({"A": series})
df.loc[:, "A"] = new_series
expected = DataFrame({"A": expected_ser})
tm.assert_frame_equal(df.isna(), expected)
tm.assert_frame_equal(df.notna(), ~expected)
class TestLoc:
def test_none_values_on_string_columns(self):
# Issue #32218
df = DataFrame(["1", "2", None], columns=["a"], dtype="str")
assert df.loc[2, "a"] is None
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_int(self, kind, request):
# int label
obj = request.getfixturevalue(f"{kind}_labels")
check_indexing_smoketest_or_raises(obj, "loc", 2, fails=KeyError)
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label(self, kind, request):
# label
obj = request.getfixturevalue(f"{kind}_empty")
check_indexing_smoketest_or_raises(obj, "loc", "c", fails=KeyError)
@pytest.mark.parametrize(
"key, typs, axes",
[
["f", ["ints", "uints", "labels", "mixed", "ts"], None],
["f", ["floats"], None],
[20, ["ints", "uints", "mixed"], None],
[20, ["labels"], None],
[20, ["ts"], 0],
[20, ["floats"], 0],
],
)
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label_out_of_range(self, key, typs, axes, kind, request):
for typ in typs:
obj = request.getfixturevalue(f"{kind}_{typ}")
# out of range label
check_indexing_smoketest_or_raises(
obj, "loc", key, axes=axes, fails=KeyError
)
@pytest.mark.parametrize(
"key, typs",
[
[[0, 1, 2], ["ints", "uints", "floats"]],
[[1, 3.0, "A"], ["ints", "uints", "floats"]],
],
)
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label_list(self, key, typs, kind, request):
for typ in typs:
obj = request.getfixturevalue(f"{kind}_{typ}")
# list of labels
check_indexing_smoketest_or_raises(obj, "loc", key, fails=KeyError)
@pytest.mark.parametrize(
"key, typs, axes",
[
[[0, 1, 2], ["empty"], None],
[[0, 2, 10], ["ints", "uints", "floats"], 0],
[[3, 6, 7], ["ints", "uints", "floats"], 1],
# GH 17758 - MultiIndex and missing keys
[[(1, 3), (1, 4), (2, 5)], ["multi"], 0],
],
)
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label_list_with_missing(self, key, typs, axes, kind, request):
for typ in typs:
obj = request.getfixturevalue(f"{kind}_{typ}")
check_indexing_smoketest_or_raises(
obj, "loc", key, axes=axes, fails=KeyError
)
@pytest.mark.parametrize("typs", ["ints", "uints"])
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label_list_fails(self, typs, kind, request):
# fails
obj = request.getfixturevalue(f"{kind}_{typs}")
check_indexing_smoketest_or_raises(
obj, "loc", [20, 30, 40], axes=1, fails=KeyError
)
def test_loc_getitem_label_array_like(self):
# TODO: test something?
# array like
pass
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_bool(self, kind, request):
obj = request.getfixturevalue(f"{kind}_empty")
# boolean indexers
b = [True, False, True, False]
check_indexing_smoketest_or_raises(obj, "loc", b, fails=IndexError)
@pytest.mark.parametrize(
"slc, typs, axes, fails",
[
[
slice(1, 3),
["labels", "mixed", "empty", "ts", "floats"],
None,
TypeError,
],
[slice("20130102", "20130104"), ["ts"], 1, TypeError],
[slice(2, 8), ["mixed"], 0, TypeError],
[slice(2, 8), ["mixed"], 1, KeyError],
[slice(2, 4, 2), ["mixed"], 0, TypeError],
],
)
@pytest.mark.parametrize("kind", ["series", "frame"])
def test_loc_getitem_label_slice(self, slc, typs, axes, fails, kind, request):
# label slices (with ints)
# real label slices
# GH 14316
for typ in typs:
obj = request.getfixturevalue(f"{kind}_{typ}")
check_indexing_smoketest_or_raises(
obj,
"loc",
slc,
axes=axes,
fails=fails,
)
def test_setitem_from_duplicate_axis(self):
# GH#34034
df = DataFrame(
[[20, "a"], [200, "a"], [200, "a"]],
columns=["col1", "col2"],
index=[10, 1, 1],
)
df.loc[1, "col1"] = np.arange(2)
expected = DataFrame(
[[20, "a"], [0, "a"], [1, "a"]], columns=["col1", "col2"], index=[10, 1, 1]
)
tm.assert_frame_equal(df, expected)
def test_column_types_consistent(self):
# GH 26779
df = DataFrame(
data={
"channel": [1, 2, 3],
"A": ["String 1", np.nan, "String 2"],
"B": [
Timestamp("2019-06-11 11:00:00"),
pd.NaT,
Timestamp("2019-06-11 12:00:00"),
],
}
)
df2 = DataFrame(
data={"A": ["String 3"], "B": [Timestamp("2019-06-11 12:00:00")]}
)
# Change Columns A and B to df2.values wherever Column A is NaN
df.loc[df["A"].isna(), ["A", "B"]] = df2.values
expected = DataFrame(
data={
"channel": [1, 2, 3],
"A": ["String 1", "String 3", "String 2"],
"B": [
Timestamp("2019-06-11 11:00:00"),
Timestamp("2019-06-11 12:00:00"),
Timestamp("2019-06-11 12:00:00"),
],
}
)
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize(
"obj, key, exp",
[
(
DataFrame([[1]], columns=Index([False])),
IndexSlice[:, False],
Series([1], name=False),
),
(Series([1], index=Index([False])), False, [1]),
(DataFrame([[1]], index=Index([False])), False, Series([1], name=False)),
],
)
def test_loc_getitem_single_boolean_arg(self, obj, key, exp):
# GH 44322
res = obj.loc[key]
if isinstance(exp, (DataFrame, Series)):
tm.assert_equal(res, exp)
else:
assert res == exp
class TestLocBaseIndependent:
# Tests for loc that do not depend on subclassing Base
def test_loc_npstr(self):
# GH#45580
df = DataFrame(index=date_range("2021", "2022"))
result = df.loc[np.array(["2021/6/1"])[0] :]
expected = df.iloc[151:]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"msg, key",
[
(r"Period\('2019', 'Y-DEC'\), 'foo', 'bar'", (Period(2019), "foo", "bar")),
(r"Period\('2019', 'Y-DEC'\), 'y1', 'bar'", (Period(2019), "y1", "bar")),
(r"Period\('2019', 'Y-DEC'\), 'foo', 'z1'", (Period(2019), "foo", "z1")),
(
r"Period\('2018', 'Y-DEC'\), Period\('2016', 'Y-DEC'\), 'bar'",
(Period(2018), Period(2016), "bar"),
),
(r"Period\('2018', 'Y-DEC'\), 'foo', 'y1'", (Period(2018), "foo", "y1")),
(
r"Period\('2017', 'Y-DEC'\), 'foo', Period\('2015', 'Y-DEC'\)",
(Period(2017), "foo", Period(2015)),
),
(r"Period\('2017', 'Y-DEC'\), 'z1', 'bar'", (Period(2017), "z1", "bar")),
],
)
def test_contains_raise_error_if_period_index_is_in_multi_index(self, msg, key):
# GH#20684
"""
parse_datetime_string_with_reso return parameter if type not matched.
PeriodIndex.get_loc takes returned value from parse_datetime_string_with_reso
as a tuple.
If first argument is Period and a tuple has 3 items,
process go on not raise exception
"""
df = DataFrame(
{
"A": [Period(2019), "x1", "x2"],
"B": [Period(2018), Period(2016), "y1"],
"C": [Period(2017), "z1", Period(2015)],
"V1": [1, 2, 3],
"V2": [10, 20, 30],
}
).set_index(["A", "B", "C"])
with pytest.raises(KeyError, match=msg):
df.loc[key]
def test_loc_getitem_missing_unicode_key(self):
df = DataFrame({"a": [1]})
with pytest.raises(KeyError, match="\u05d0"):
df.loc[:, "\u05d0"] # should not raise UnicodeEncodeError
def test_loc_getitem_dups(self):
# GH 5678
# repeated getitems on a dup index returning a ndarray
df = DataFrame(
np.random.default_rng(2).random((20, 5)),
index=["ABCDE"[x % 5] for x in range(20)],
)
expected = df.loc["A", 0]
result = df.loc[:, 0].loc["A"]
tm.assert_series_equal(result, expected)
def test_loc_getitem_dups2(self):
# GH4726
# dup indexing with iloc/loc
df = DataFrame(
[[1, 2, "foo", "bar", Timestamp("20130101")]],
columns=["a", "a", "a", "a", "a"],
index=[1],
)
expected = Series(
[1, 2, "foo", "bar", Timestamp("20130101")],
index=["a", "a", "a", "a", "a"],
name=1,
)
result = df.iloc[0]
tm.assert_series_equal(result, expected)
result = df.loc[1]
tm.assert_series_equal(result, expected)
def test_loc_setitem_dups(self):
# GH 6541
df_orig = DataFrame(
{
"me": list("rttti"),
"foo": list("aaade"),
"bar": np.arange(5, dtype="float64") * 1.34 + 2,
"bar2": np.arange(5, dtype="float64") * -0.34 + 2,
}
).set_index("me")
indexer = (
"r",
["bar", "bar2"],
)
df = df_orig.copy()
df.loc[indexer] *= 2.0
tm.assert_series_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer])
indexer = (
"r",
"bar",
)
df = df_orig.copy()
df.loc[indexer] *= 2.0
assert df.loc[indexer] == 2.0 * df_orig.loc[indexer]
indexer = (
"t",
["bar", "bar2"],
)
df = df_orig.copy()
df.loc[indexer] *= 2.0
tm.assert_frame_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer])
def test_loc_setitem_slice(self):
# GH10503
# assigning the same type should not change the type
df1 = DataFrame({"a": [0, 1, 1], "b": Series([100, 200, 300], dtype="uint32")})
ix = df1["a"] == 1
newb1 = df1.loc[ix, "b"] + 1
df1.loc[ix, "b"] = newb1
expected = DataFrame(
{"a": [0, 1, 1], "b": Series([100, 201, 301], dtype="uint32")}
)
tm.assert_frame_equal(df1, expected)
# assigning a new type should get the inferred type
df2 = DataFrame({"a": [0, 1, 1], "b": [100, 200, 300]}, dtype="uint64")
ix = df1["a"] == 1
newb2 = df2.loc[ix, "b"]
with tm.assert_produces_warning(
FutureWarning, match="item of incompatible dtype"
):
df1.loc[ix, "b"] = newb2
expected = DataFrame({"a": [0, 1, 1], "b": [100, 200, 300]}, dtype="uint64")
tm.assert_frame_equal(df2, expected)
def test_loc_setitem_dtype(self):
# GH31340
df = DataFrame({"id": ["A"], "a": [1.2], "b": [0.0], "c": [-2.5]})
cols = ["a", "b", "c"]
df.loc[:, cols] = df.loc[:, cols].astype("float32")
# pre-2.0 this setting would swap in new arrays, in 2.0 it is correctly
# in-place, consistent with non-split-path
expected = DataFrame(
{
"id": ["A"],
"a": np.array([1.2], dtype="float64"),
"b": np.array([0.0], dtype="float64"),
"c": np.array([-2.5], dtype="float64"),
}
) # id is inferred as object
tm.assert_frame_equal(df, expected)
def test_getitem_label_list_with_missing(self):
s = Series(range(3), index=["a", "b", "c"])
# consistency
with pytest.raises(KeyError, match="not in index"):
s[["a", "d"]]
s = Series(range(3))
with pytest.raises(KeyError, match="not in index"):
s[[0, 3]]
@pytest.mark.parametrize("index", [[True, False], [True, False, True, False]])
def test_loc_getitem_bool_diff_len(self, index):
# GH26658
s = Series([1, 2, 3])
msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}"
with pytest.raises(IndexError, match=msg):
s.loc[index]
def test_loc_getitem_int_slice(self):
# TODO: test something here?
pass
def test_loc_to_fail(self):
# GH3449
df = DataFrame(
np.random.default_rng(2).random((3, 3)),
index=["a", "b", "c"],
columns=["e", "f", "g"],
)
msg = (
rf"\"None of \[Index\(\[1, 2\], dtype='{np.dtype(int)}'\)\] are "
r"in the \[index\]\""
)
with pytest.raises(KeyError, match=msg):
df.loc[[1, 2], [1, 2]]
def test_loc_to_fail2(self):
# GH 7496
# loc should not fallback
s = Series(dtype=object)
s.loc[1] = 1
s.loc["a"] = 2
with pytest.raises(KeyError, match=r"^-1$"):
s.loc[-1]
msg = (
rf"\"None of \[Index\(\[-1, -2\], dtype='{np.dtype(int)}'\)\] are "
r"in the \[index\]\""
)
with pytest.raises(KeyError, match=msg):
s.loc[[-1, -2]]
msg = r"\"None of \[Index\(\['4'\], dtype='object'\)\] are in the \[index\]\""
with pytest.raises(KeyError, match=msg):
s.loc[Index(["4"], dtype=object)]
s.loc[-1] = 3
with pytest.raises(KeyError, match="not in index"):
s.loc[[-1, -2]]
s["a"] = 2
msg = (
rf"\"None of \[Index\(\[-2\], dtype='{np.dtype(int)}'\)\] are "
r"in the \[index\]\""
)
with pytest.raises(KeyError, match=msg):
s.loc[[-2]]
del s["a"]
with pytest.raises(KeyError, match=msg):
s.loc[[-2]] = 0
def test_loc_to_fail3(self):
# inconsistency between .loc[values] and .loc[values,:]
# GH 7999
df = DataFrame([["a"], ["b"]], index=[1, 2], columns=["value"])
msg = (
rf"\"None of \[Index\(\[3\], dtype='{np.dtype(int)}'\)\] are "
r"in the \[index\]\""
)
with pytest.raises(KeyError, match=msg):
df.loc[[3], :]
with pytest.raises(KeyError, match=msg):
df.loc[[3]]
def test_loc_getitem_list_with_fail(self):
# 15747
# should KeyError if *any* missing labels
s = Series([1, 2, 3])
s.loc[[2]]
msg = f"\"None of [Index([3], dtype='{np.dtype(int)}')] are in the [index]"
with pytest.raises(KeyError, match=re.escape(msg)):
s.loc[[3]]
# a non-match and a match
with pytest.raises(KeyError, match="not in index"):
s.loc[[2, 3]]
def test_loc_index(self):
# gh-17131
# a boolean index should index like a boolean numpy array
df = DataFrame(
np.random.default_rng(2).random(size=(5, 10)),
index=["alpha_0", "alpha_1", "alpha_2", "beta_0", "beta_1"],
)
mask = df.index.map(lambda x: "alpha" in x)
expected = df.loc[np.array(mask)]
result = df.loc[mask]
tm.assert_frame_equal(result, expected)
result = df.loc[mask.values]
tm.assert_frame_equal(result, expected)
result = df.loc[pd.array(mask, dtype="boolean")]
tm.assert_frame_equal(result, expected)
def test_loc_general(self):
df = DataFrame(
np.random.default_rng(2).random((4, 4)),
columns=["A", "B", "C", "D"],
index=["A", "B", "C", "D"],
)
# want this to work
result = df.loc[:, "A":"B"].iloc[0:2, :]
assert (result.columns == ["A", "B"]).all()
assert (result.index == ["A", "B"]).all()
# 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()
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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()
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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()
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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")])})
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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)
# before the enforcement of #45333 in 2.0, the loc.setitem here would
# change the dtype of df.x to int64
expected = DataFrame(columns=["x", "y"])
df = DataFrame(columns=["x", "y"])
with tm.assert_produces_warning(None):
df.loc[:, "x"] = 1
tm.assert_frame_equal(df, expected)
# setting with setitem swaps in a new array, so changes the dtype
df = DataFrame(columns=["x", "y"])
df["x"] = 1
expected["x"] = expected["x"].astype(np.int64)
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 = df.infer_objects(copy=False)
# Adding a new key
df.loc[:, ("Respondent", "Duration")] = (
df.loc[:, ("Respondent", "EndDate")]
- df.loc[:, ("Respondent", "StartDate")]
)
# timedelta64[m] -> float, so this cannot be done inplace, so
# no warning
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
df.loc[:, ("Respondent", "Duration")] = df.loc[
:, ("Respondent", "Duration")
] / Timedelta(60_000_000_000)
expected = Series(
[23.0, 12.0, 14.0, 36.0], 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):
# 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)
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")
# pre-2.0 this setting swapped in a new array, now it is inplace
# consistent with non-split-path
expected = DataFrame({"A": ser.astype(float)})
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")
# pre-2.0 this setting swapped in a new array, now it is inplace
# consistent with non-split-path
expected = DataFrame({"A": [3.0, 2.0, 1.0], "B": "string"}, index=[1, 2, 3])
tm.assert_frame_equal(df, expected)
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}, columns=Index(["A", "B"], dtype=object)
).reindex(index=index)
tm.assert_frame_equal(df, expected)
def test_loc_setitem_frame(self):
df = DataFrame(
np.random.default_rng(2).standard_normal((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)
@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
def test_loc_setitem_frame_multiples(self, warn_copy_on_write):
# 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'])".
# Set object dtype to avoid upcast when setting 'Z'
df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]).astype({"A": object})
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
df = DataFrame({"date": [datetime(2012, 1, 1), 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=Index(["foo", "bar"], dtype=object))
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
@pytest.mark.parametrize("length, l2", [[900, 100], [900000, 100000]])
def test_loc_non_unique_memory_error(self, length, l2):
# GH 4280
# non_unique index with a large selection triggers a memory error
columns = list("ABCDEFG")
df = pd.concat(
[
DataFrame(
np.random.default_rng(2).standard_normal((length, len(columns))),
index=np.arange(length),
columns=columns,
),
DataFrame(np.ones((l2, len(columns))), index=[0] * l2, columns=columns),
]
)
assert df.index.is_unique is False
mask = np.arange(l2)
result = df.loc[mask]
expected = pd.concat(
[
df.take([0]),
DataFrame(
np.ones((len(mask), len(columns))),
index=[0] * len(mask),
columns=columns,
),
df.take(mask[1:]),
]
)
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 = DataFrame(
np.ones((5, 2)),
index=Index([f"i-{i}" for i in range(5)], name="a"),
columns=Index([f"i-{i}" for i in range(2)], name="a"),
)
# 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_copy_on_write, warn_copy_on_write
):
# GH13873
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
assert original_df.loc[:, :] 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
# depending on CoW
with tm.assert_cow_warning(warn_copy_on_write):
original_df.loc[:, "a"] = [4, 4, 4]
if using_copy_on_write:
assert (sliced_df["a"] == [1, 2, 3]).all()
else:
assert (sliced_df["a"] == 4).all()
# These should not return copies
df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
if using_copy_on_write or warn_copy_on_write:
assert df[0] is not df.loc[:, 0]
else:
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
with tm.assert_cow_warning(warn_copy_on_write):
original_series[:3] = [7, 8, 9]
if using_copy_on_write:
assert all(sliced_series[:3] == [1, 2, 3])
else:
assert all(sliced_series[:3] == [7, 8, 9])
def test_loc_copy_vs_view(self, request, using_copy_on_write):
# GH 15631
if not using_copy_on_write:
mark = pytest.mark.xfail(reason="accidental fix reverted - GH37497")
request.applymarker(mark)
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": np.array([np.nan] * len(data), dtype=object)}
)
# 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": np.array([np.nan] * len(data), dtype=object)}
)
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 = (
rf"None of \[Index\(\[0, 1\], dtype='{np.dtype(int)}'\)\] "
r"are in the \[index\]"
)
with pytest.raises(KeyError, match=msg):
df.loc[[0, 1], "x"] = data
msg = "setting an array element with a sequence."
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)
@pytest.mark.xfail(using_pyarrow_string_dtype(), reason="can't set int into string")
def test_loc_setitem_str_to_small_float_conversion_type(self):
# GH#20388
col_data = [str(np.random.default_rng(2).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.default_rng(2).standard_normal((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])
def test_loc_getitem_range_from_spmatrix(self, spmatrix_t, dtype):
sp_sparse = pytest.importorskip("scipy.sparse")
spmatrix_t = getattr(sp_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)
def test_loc_getitem_sparse_frame(self):
# GH34687
sp_sparse = pytest.importorskip("scipy.sparse")
df = DataFrame.sparse.from_spmatrix(sp_sparse.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("indexer", ["loc", "iloc"])
def test_getitem_single_row_sparse_df(self, indexer):
# GH#46406
df = DataFrame([[1.0, 0.0, 1.5], [0.0, 2.0, 0.0]], dtype=SparseDtype(float))
result = getattr(df, indexer)[0]
expected = Series([1.0, 0.0, 1.5], dtype=SparseDtype(float), name=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.default_rng(2).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_float_index(self, float_numpy_dtype):
# note labels are floats
dtype = float_numpy_dtype
ser = Series(["a", "b", "c"], index=Index([0, 0.5, 1], dtype=dtype))
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")})
with tm.assert_produces_warning(
FutureWarning, match="item of incompatible dtype"
):
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, using_infer_string):
# GH#25495
df = DataFrame({"Alpha": ["a"], "Numeric": [0]})
categories = Categorical(df["Alpha"], categories=["a", "b", "c"])
# pre-2.0 this swapped in a new array, in 2.0 it operates inplace,
# consistent with non-split-path
df.loc[:, "Alpha"] = categories
result = df["Alpha"]
expected = Series(categories, index=df.index, name="Alpha").astype(
object if not using_infer_string else "string[pyarrow_numpy]"
)
tm.assert_series_equal(result, expected)
# double-check that the non-loc setting retains categoricalness
df["Alpha"] = categories
tm.assert_series_equal(df["Alpha"], Series(categories, name="Alpha"))
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"]
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
df.loc[2, "c"] = date(2005, 5, 5)
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)
with tm.assert_produces_warning(
FutureWarning if idxer == "var" else None, match="incompatible dtype"
):
# See https://github.com/pandas-dev/pandas/issues/56223
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.default_rng(2).standard_normal((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="int64")
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="int64")
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.default_rng(2).standard_normal((2, 2))
# float64 dtype to avoid upcast when trying to set float data
ser = Series(range(2), dtype="float64")
msg = "setting an array element with a sequence."
with pytest.raises(ValueError, match=msg):
ser.loc[range(2)] = data
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, using_infer_string):
df = DataFrame(
np.random.default_rng(2).standard_normal((5, 3)),
index=["a", "b", "c", "d", "e"],
columns=["foo", "bar", "baz"],
)
df["str"] = "qux"
df.loc[df.index[::2], "str"] = np.nan
expected = Series(
[np.nan, "qux", np.nan, "qux", np.nan],
dtype=object if not using_infer_string else "string[pyarrow_numpy]",
).values
tm.assert_almost_equal(df["str"].values, expected)
def test_loc_setitem_cast2(self):
# GH#7704
# dtype conversion on setting
df = DataFrame(np.random.default_rng(2).random((30, 3)), columns=tuple("ABC"))
df["event"] = np.nan
with tm.assert_produces_warning(
FutureWarning, match="item of incompatible dtype"
):
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)
def test_loc_setitem_range_key(self, frame_or_series):
# GH#45479 don't treat range key as positional
obj = frame_or_series(range(5), index=[3, 4, 1, 0, 2])
values = [9, 10, 11]
if obj.ndim == 2:
values = [[9], [10], [11]]
obj.loc[range(3)] = values
expected = frame_or_series([0, 1, 10, 9, 11], index=obj.index)
tm.assert_equal(obj, expected)
def test_loc_setitem_numpy_frame_categorical_value(self):
# GH#52927
df = DataFrame({"a": [1, 1, 1, 1, 1], "b": ["a", "a", "a", "a", "a"]})
df.loc[1:2, "a"] = Categorical([2, 2], categories=[1, 2])
expected = DataFrame({"a": [1, 2, 2, 1, 1], "b": ["a", "a", "a", "a", "a"]})
tm.assert_frame_equal(df, expected)
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)
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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.inferred_type == "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_additional_element_to_categorical_series_loc(self):
# GH#47677
result = Series(["a", "b", "c"], dtype="category")
result.loc[3] = 0
expected = Series(["a", "b", "c", 0], dtype="object")
tm.assert_series_equal(result, expected)
def test_additional_categorical_element_loc(self):
# GH#47677
result = Series(["a", "b", "c"], dtype="category")
result.loc[3] = "a"
expected = Series(["a", "b", "c", "a"], dtype="category")
tm.assert_series_equal(result, expected)
def test_loc_set_nan_in_categorical_series(self, any_numeric_ea_dtype):
# GH#47677
srs = Series(
[1, 2, 3],
dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)),
)
# enlarge
srs.loc[3] = np.nan
expected = Series(
[1, 2, 3, np.nan],
dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)),
)
tm.assert_series_equal(srs, expected)
# set into
srs.loc[1] = np.nan
expected = Series(
[1, np.nan, 3, np.nan],
dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)),
)
tm.assert_series_equal(srs, expected)
@pytest.mark.parametrize("na", (np.nan, pd.NA, None, pd.NaT))
def test_loc_consistency_series_enlarge_set_into(self, na):
# GH#47677
srs_enlarge = Series(["a", "b", "c"], dtype="category")
srs_enlarge.loc[3] = na
srs_setinto = Series(["a", "b", "c", "a"], dtype="category")
srs_setinto.loc[3] = na
tm.assert_series_equal(srs_enlarge, srs_setinto)
expected = Series(["a", "b", "c", na], dtype="category")
tm.assert_series_equal(srs_enlarge, 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:
def test_loc_setitem_with_expansion_large_dataframe(self, monkeypatch):
# GH#10692
size_cutoff = 50
with monkeypatch.context():
monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff)
result = DataFrame({"x": range(size_cutoff)}, dtype="int64")
result.loc[size_cutoff] = size_cutoff
expected = DataFrame({"x": range(size_cutoff + 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]))
def test_loc_setitem_empty_series_float(self):
# GH#5226
# partially set with an empty object series
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]))
def test_loc_setitem_empty_series_str_idx(self):
# GH#5226
# partially set with an empty object series
ser = Series(dtype=object)
ser.loc["foo"] = 1
tm.assert_series_equal(ser, Series([1], index=Index(["foo"], dtype=object)))
ser.loc["bar"] = 3
tm.assert_series_equal(
ser, Series([1, 3], index=Index(["foo", "bar"], dtype=object))
)
ser.loc[3] = 4
tm.assert_series_equal(
ser, Series([1, 3, 4], index=Index(["foo", "bar", 3], dtype=object))
)
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)
@pytest.mark.parametrize(
"conv",
[
lambda x: x,
lambda x: x.to_datetime64(),
lambda x: x.to_pydatetime(),
lambda x: np.datetime64(x),
],
ids=["self", "to_datetime64", "to_pydatetime", "np.datetime64"],
)
def test_loc_setitem_datetime_keys_cast(self, conv):
# GH#9516
dt1 = Timestamp("20130101 09:00:00")
dt2 = Timestamp("20130101 10:00:00")
df = DataFrame()
df.loc[conv(dt1), "one"] = 100
df.loc[conv(dt2), "one"] = 200
expected = DataFrame(
{"one": [100.0, 200.0]},
index=[dt1, dt2],
columns=Index(["one"], dtype=object),
)
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")
# pre-2.0 trying to set a single element on a part of a different
# timezone converted to object; in 2.0 it retains dtype
df2 = df.copy()
df2.loc[df2.new_col == "new", "time"] = v
expected = Series([v[0].tz_convert("UTC"), 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 = Index([0, 1, np.inf], dtype=np.float64)
tm.assert_index_equal(result, expected)
@pytest.mark.filterwarnings("ignore:indexing past lexsort depth")
def test_loc_setitem_with_expansion_nonunique_index(self, index):
# GH#40096
if not len(index):
pytest.skip("Not relevant for empty Index")
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, check_column_type=False)
result = DataFrame(index=df.index)
result.loc[df.index, "data"] = ser._values
tm.assert_frame_equal(result, df, check_column_type=False)
def test_loc_setitem_ea_not_full_column(self):
# GH#39163
df = DataFrame({"A": range(5)})
val = date_range("2016-01-01", periods=3, tz="US/Pacific")
df.loc[[0, 1, 2], "B"] = val
bex = val.append(DatetimeIndex([pd.NaT, pd.NaT], dtype=val.dtype))
expected = DataFrame({"A": range(5), "B": bex})
assert expected.dtypes["B"] == val.dtype
tm.assert_frame_equal(df, expected)
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": Series(list("aabb"), dtype=object)},
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.default_rng(2).random((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_floatindex(self, float_numpy_dtype):
dtype = float_numpy_dtype
ser = Series(
np.random.default_rng(2).random(10), index=np.arange(10, 20, dtype=dtype)
)
assert len(ser.loc[12.0:]) == 8
assert len(ser.loc[12.5:]) == 7
idx = np.arange(10, 20, dtype=dtype)
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 pytest.raises(
KeyError, match="Value based partial slicing on non-monotonic"
):
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:
@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
values = expected.values.copy()
values[mask.values, 1] = 0
expected = DataFrame(values, index=expected.index, columns=expected.columns)
tm.assert_frame_equal(float_frame, expected)
def test_loc_setitem_ndframe_values_alignment(
self, using_copy_on_write, warn_copy_on_write
):
# GH#45501
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.loc[[False, False, True], ["a"]] = DataFrame(
{"a": [10, 20, 30]}, index=[2, 1, 0]
)
expected = DataFrame({"a": [1, 2, 10], "b": [4, 5, 6]})
tm.assert_frame_equal(df, expected)
# same thing with Series RHS
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.loc[[False, False, True], ["a"]] = Series([10, 11, 12], index=[2, 1, 0])
tm.assert_frame_equal(df, expected)
# same thing but setting "a" instead of ["a"]
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.loc[[False, False, True], "a"] = Series([10, 11, 12], index=[2, 1, 0])
tm.assert_frame_equal(df, expected)
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df_orig = df.copy()
ser = df["a"]
with tm.assert_cow_warning(warn_copy_on_write):
ser.loc[[False, False, True]] = Series([10, 11, 12], index=[2, 1, 0])
if using_copy_on_write:
tm.assert_frame_equal(df, df_orig)
else:
tm.assert_frame_equal(df, expected)
def test_loc_indexer_empty_broadcast(self):
# GH#51450
df = DataFrame({"a": [], "b": []}, dtype=object)
expected = df.copy()
df.loc[np.array([], dtype=np.bool_), ["a"]] = df["a"].copy()
tm.assert_frame_equal(df, expected)
def test_loc_indexer_all_false_broadcast(self):
# GH#51450
df = DataFrame({"a": ["x"], "b": ["y"]}, dtype=object)
expected = df.copy()
df.loc[np.array([False], dtype=np.bool_), ["a"]] = df["b"].copy()
tm.assert_frame_equal(df, expected)
def test_loc_indexer_length_one(self):
# GH#51435
df = DataFrame({"a": ["x"], "b": ["y"]}, dtype=object)
expected = DataFrame({"a": ["y"], "b": ["y"]}, dtype=object)
df.loc[np.array([True], dtype=np.bool_), ["a"]] = df["b"].copy()
tm.assert_frame_equal(df, 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.default_rng(2).random((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.default_rng(2).standard_normal((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_raises():
# GH#31840
ser = Series(range(4), index=["A", "B", "C", "D"])
with pytest.raises(TypeError, match="Slicing a positional slice with .loc"):
ser.loc[:3] = 2
def test_loc_slice_disallows_positional():
# GH#16121, GH#24612, GH#31810
dti = date_range("2016-01-01", periods=3)
df = DataFrame(np.random.default_rng(2).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 pytest.raises(TypeError, match="Slicing a positional slice with .loc"):
# GH#31840 enforce incorrect behavior
obj.loc[1:3] = 1
with pytest.raises(TypeError, match=msg):
df.loc[1:3, 1]
with pytest.raises(TypeError, match="Slicing a positional slice with .loc"):
# GH#31840 enforce 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.default_rng(2).standard_normal((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.default_rng(2).standard_normal((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_setitem_multiindex_timestamp():
# GH#13831
vals = np.random.default_rng(2).standard_normal((8, 6))
idx = date_range("1/1/2000", periods=8)
cols = ["A", "B", "C", "D", "E", "F"]
exp = DataFrame(vals, index=idx, columns=cols)
exp.loc[exp.index[1], ("A", "B")] = np.nan
vals[1][0:2] = np.nan
res = DataFrame(vals, index=idx, columns=cols)
tm.assert_frame_equal(res, exp)
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)
@pytest.mark.parametrize("value", [300, np.uint16(300), np.int16(300)])
def test_loc_setitem_uint8_upcast(value):
# GH#26049
df = DataFrame([1, 2, 3, 4], columns=["col1"], dtype="uint8")
with tm.assert_produces_warning(FutureWarning, match="item of incompatible dtype"):
df.loc[2, "col1"] = value # value that can't be held in uint8
if np_version_gt2 and isinstance(value, np.int16):
# Note, result type of uint8 + int16 is int16
# in numpy < 2, though, numpy would inspect the
# value and see that it could fit in an uint16, resulting in a uint16
dtype = "int16"
else:
dtype = "uint16"
expected = DataFrame([1, 2, 300, 4], columns=["col1"], dtype=dtype)
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize(
"fill_val,exp_dtype",
[
(Timestamp("2022-01-06"), "datetime64[ns]"),
(Timestamp("2022-01-07", tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
],
)
def test_loc_setitem_using_datetimelike_str_as_index(fill_val, exp_dtype):
data = ["2022-01-02", "2022-01-03", "2022-01-04", fill_val.date()]
index = DatetimeIndex(data, tz=fill_val.tz, dtype=exp_dtype)
df = DataFrame([10, 11, 12, 14], columns=["a"], index=index)
# adding new row using an unexisting datetime-like str index
df.loc["2022-01-08", "a"] = 13
data.append("2022-01-08")
expected_index = DatetimeIndex(data, dtype=exp_dtype)
tm.assert_index_equal(df.index, expected_index, exact=True)
def test_loc_set_int_dtype():
# GH#23326
df = DataFrame([list("abc")])
df.loc[:, "col1"] = 5
expected = DataFrame({0: ["a"], 1: ["b"], 2: ["c"], "col1": [5]})
tm.assert_frame_equal(df, expected)
@pytest.mark.filterwarnings(r"ignore:Period with BDay freq is deprecated:FutureWarning")
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
def test_loc_periodindex_3_levels():
# GH#24091
p_index = PeriodIndex(
["20181101 1100", "20181101 1200", "20181102 1300", "20181102 1400"],
name="datetime",
freq="B",
)
mi_series = DataFrame(
[["A", "B", 1.0], ["A", "C", 2.0], ["Z", "Q", 3.0], ["W", "F", 4.0]],
index=p_index,
columns=["ONE", "TWO", "VALUES"],
)
mi_series = mi_series.set_index(["ONE", "TWO"], append=True)["VALUES"]
assert mi_series.loc[(p_index[0], "A", "B")] == 1.0
def test_loc_setitem_pyarrow_strings():
# GH#52319
pytest.importorskip("pyarrow")
df = DataFrame(
{
"strings": Series(["A", "B", "C"], dtype="string[pyarrow]"),
"ids": Series([True, True, False]),
}
)
new_value = Series(["X", "Y"])
df.loc[df.ids, "strings"] = new_value
expected_df = DataFrame(
{
"strings": Series(["X", "Y", "C"], dtype="string[pyarrow]"),
"ids": Series([True, True, False]),
}
)
tm.assert_frame_equal(df, expected_df)
class TestLocSeries:
@pytest.mark.parametrize("val,expected", [(2**63 - 1, 3), (2**63, 4)])
def test_loc_uint64(self, val, expected):
# see GH#19399
ser = Series({2**63 - 1: 3, 2**63: 4})
assert ser.loc[val] == expected
def test_loc_getitem(self, string_series, datetime_series):
inds = string_series.index[[3, 4, 7]]
tm.assert_series_equal(string_series.loc[inds], string_series.reindex(inds))
tm.assert_series_equal(string_series.iloc[5::2], string_series[5::2])
# slice with indices
d1, d2 = datetime_series.index[[5, 15]]
result = datetime_series.loc[d1:d2]
expected = datetime_series.truncate(d1, d2)
tm.assert_series_equal(result, expected)
# boolean
mask = string_series > string_series.median()
tm.assert_series_equal(string_series.loc[mask], string_series[mask])
# ask for index value
assert datetime_series.loc[d1] == datetime_series[d1]
assert datetime_series.loc[d2] == datetime_series[d2]
def test_loc_getitem_not_monotonic(self, datetime_series):
d1, d2 = datetime_series.index[[5, 15]]
ts2 = datetime_series[::2].iloc[[1, 2, 0]]
msg = r"Timestamp\('2000-01-10 00:00:00'\)"
with pytest.raises(KeyError, match=msg):
ts2.loc[d1:d2]
with pytest.raises(KeyError, match=msg):
ts2.loc[d1:d2] = 0
def test_loc_getitem_setitem_integer_slice_keyerrors(self):
ser = Series(
np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
)
# this is OK
cp = ser.copy()
cp.iloc[4:10] = 0
assert (cp.iloc[4:10] == 0).all()
# so is this
cp = ser.copy()
cp.iloc[3:11] = 0
assert (cp.iloc[3:11] == 0).values.all()
result = ser.iloc[2:6]
result2 = ser.loc[3:11]
expected = ser.reindex([4, 6, 8, 10])
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result2, expected)
# non-monotonic, raise KeyError
s2 = ser.iloc[list(range(5)) + list(range(9, 4, -1))]
with pytest.raises(KeyError, match=r"^3$"):
s2.loc[3:11]
with pytest.raises(KeyError, match=r"^3$"):
s2.loc[3:11] = 0
def test_loc_getitem_iterator(self, string_series):
idx = iter(string_series.index[:10])
result = string_series.loc[idx]
tm.assert_series_equal(result, string_series[:10])
def test_loc_setitem_boolean(self, string_series):
mask = string_series > string_series.median()
result = string_series.copy()
result.loc[mask] = 0
expected = string_series
expected[mask] = 0
tm.assert_series_equal(result, expected)
def test_loc_setitem_corner(self, string_series):
inds = list(string_series.index[[5, 8, 12]])
string_series.loc[inds] = 5
msg = r"\['foo'\] not in index"
with pytest.raises(KeyError, match=msg):
string_series.loc[inds + ["foo"]] = 5
def test_basic_setitem_with_labels(self, datetime_series):
indices = datetime_series.index[[5, 10, 15]]
cp = datetime_series.copy()
exp = datetime_series.copy()
cp[indices] = 0
exp.loc[indices] = 0
tm.assert_series_equal(cp, exp)
cp = datetime_series.copy()
exp = datetime_series.copy()
cp[indices[0] : indices[2]] = 0
exp.loc[indices[0] : indices[2]] = 0
tm.assert_series_equal(cp, exp)
def test_loc_setitem_listlike_of_ints(self):
# integer indexes, be careful
ser = Series(
np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2))
)
inds = [0, 4, 6]
arr_inds = np.array([0, 4, 6])
cp = ser.copy()
exp = ser.copy()
ser[inds] = 0
ser.loc[inds] = 0
tm.assert_series_equal(cp, exp)
cp = ser.copy()
exp = ser.copy()
ser[arr_inds] = 0
ser.loc[arr_inds] = 0
tm.assert_series_equal(cp, exp)
inds_notfound = [0, 4, 5, 6]
arr_inds_notfound = np.array([0, 4, 5, 6])
msg = r"\[5\] not in index"
with pytest.raises(KeyError, match=msg):
ser[inds_notfound] = 0
with pytest.raises(Exception, match=msg):
ser[arr_inds_notfound] = 0
def test_loc_setitem_dt64tz_values(self):
# GH#12089
ser = Series(
date_range("2011-01-01", periods=3, tz="US/Eastern"),
index=["a", "b", "c"],
)
s2 = ser.copy()
expected = Timestamp("2011-01-03", tz="US/Eastern")
s2.loc["a"] = expected
result = s2.loc["a"]
assert result == expected
s2 = ser.copy()
s2.iloc[0] = expected
result = s2.iloc[0]
assert result == expected
s2 = ser.copy()
s2["a"] = expected
result = s2["a"]
assert result == expected
@pytest.mark.parametrize("array_fn", [np.array, pd.array, list, tuple])
@pytest.mark.parametrize("size", [0, 4, 5, 6])
def test_loc_iloc_setitem_with_listlike(self, size, array_fn):
# GH37748
# testing insertion, in a Series of size N (here 5), of a listlike object
# of size 0, N-1, N, N+1
arr = array_fn([0] * size)
expected = Series([arr, 0, 0, 0, 0], index=list("abcde"), dtype=object)
ser = Series(0, index=list("abcde"), dtype=object)
ser.loc["a"] = arr
tm.assert_series_equal(ser, expected)
ser = Series(0, index=list("abcde"), dtype=object)
ser.iloc[0] = arr
tm.assert_series_equal(ser, expected)
@pytest.mark.parametrize("indexer", [IndexSlice["A", :], ("A", slice(None))])
def test_loc_series_getitem_too_many_dimensions(self, indexer):
# GH#35349
ser = Series(
index=MultiIndex.from_tuples([("A", "0"), ("A", "1"), ("B", "0")]),
data=[21, 22, 23],
)
msg = "Too many indexers"
with pytest.raises(IndexingError, match=msg):
ser.loc[indexer, :]
with pytest.raises(IndexingError, match=msg):
ser.loc[indexer, :] = 1
def test_loc_setitem(self, string_series):
inds = string_series.index[[3, 4, 7]]
result = string_series.copy()
result.loc[inds] = 5
expected = string_series.copy()
expected.iloc[[3, 4, 7]] = 5
tm.assert_series_equal(result, expected)
result.iloc[5:10] = 10
expected[5:10] = 10
tm.assert_series_equal(result, expected)
# set slice with indices
d1, d2 = string_series.index[[5, 15]]
result.loc[d1:d2] = 6
expected[5:16] = 6 # because it's inclusive
tm.assert_series_equal(result, expected)
# set index value
string_series.loc[d1] = 4
string_series.loc[d2] = 6
assert string_series[d1] == 4
assert string_series[d2] == 6
@pytest.mark.parametrize("dtype", ["object", "string"])
def test_loc_assign_dict_to_row(self, dtype):
# GH41044
df = DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype=dtype)
df.loc[0, :] = {"A": "newA", "B": "newB"}
expected = DataFrame({"A": ["newA", "def"], "B": ["newB", "jkl"]}, dtype=dtype)
tm.assert_frame_equal(df, expected)
@td.skip_array_manager_invalid_test
def test_loc_setitem_dict_timedelta_multiple_set(self):
# GH 16309
result = DataFrame(columns=["time", "value"])
result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"}
result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"}
expected = DataFrame(
[[Timedelta(6, unit="s"), "foo"]], columns=["time", "value"], index=[1]
)
tm.assert_frame_equal(result, expected)
def test_loc_set_multiple_items_in_multiple_new_columns(self):
# GH 25594
df = DataFrame(index=[1, 2], columns=["a"])
df.loc[1, ["b", "c"]] = [6, 7]
expected = DataFrame(
{
"a": Series([np.nan, np.nan], dtype="object"),
"b": [6, np.nan],
"c": [7, np.nan],
},
index=[1, 2],
)
tm.assert_frame_equal(df, expected)
def test_getitem_loc_str_periodindex(self):
# GH#33964
msg = "Period with BDay freq is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
index = pd.period_range(start="2000", periods=20, freq="B")
series = Series(range(20), index=index)
assert series.loc["2000-01-14"] == 9
def test_loc_nonunique_masked_index(self):
# GH 57027
ids = list(range(11))
index = Index(ids * 1000, dtype="Int64")
df = DataFrame({"val": np.arange(len(index), dtype=np.intp)}, index=index)
result = df.loc[ids]
expected = DataFrame(
{"val": index.argsort(kind="stable").astype(np.intp)},
index=Index(np.array(ids).repeat(1000), dtype="Int64"),
)
tm.assert_frame_equal(result, expected)
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