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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/venv/lib/python3.9/site-packages/pandas/tests/indexing/test_loc.py

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Refactor on docs; Add performance comparison
4 years ago
""" test label based indexing with loc """
from io import StringIO
import re
import numpy as np
import pytest
from pandas.compat.numpy import _is_numpy_dev
import pandas as pd
from pandas import DataFrame, Series, Timestamp, date_range
import pandas._testing as tm
from pandas.api.types import is_scalar
from pandas.tests.indexing.common import Base
class TestLoc(Base):
def test_loc_getitem_int(self):
# int label
self.check_result("loc", 2, typs=["labels"], fails=KeyError)
def test_loc_getitem_label(self):
# label
self.check_result("loc", "c", typs=["empty"], fails=KeyError)
def test_loc_getitem_label_out_of_range(self):
# out of range label
self.check_result(
"loc", "f", typs=["ints", "uints", "labels", "mixed", "ts"], fails=KeyError,
)
self.check_result("loc", "f", typs=["floats"], fails=KeyError)
self.check_result("loc", "f", typs=["floats"], fails=KeyError)
self.check_result(
"loc", 20, typs=["ints", "uints", "mixed"], fails=KeyError,
)
self.check_result("loc", 20, typs=["labels"], fails=KeyError)
self.check_result("loc", 20, typs=["ts"], axes=0, fails=KeyError)
self.check_result("loc", 20, typs=["floats"], axes=0, fails=KeyError)
def test_loc_getitem_label_list(self):
# TODO: test something here?
# list of labels
pass
def test_loc_getitem_label_list_with_missing(self):
self.check_result(
"loc", [0, 1, 2], typs=["empty"], fails=KeyError,
)
self.check_result(
"loc", [0, 2, 10], typs=["ints", "uints", "floats"], axes=0, fails=KeyError,
)
self.check_result(
"loc", [3, 6, 7], typs=["ints", "uints", "floats"], axes=1, fails=KeyError,
)
# GH 17758 - MultiIndex and missing keys
self.check_result(
"loc", [(1, 3), (1, 4), (2, 5)], typs=["multi"], axes=0, fails=KeyError,
)
def test_loc_getitem_label_list_fails(self):
# fails
self.check_result(
"loc", [20, 30, 40], typs=["ints", "uints"], axes=1, fails=KeyError,
)
def test_loc_getitem_label_array_like(self):
# TODO: test something?
# array like
pass
def test_loc_getitem_bool(self):
# boolean indexers
b = [True, False, True, False]
self.check_result("loc", b, typs=["empty"], fails=IndexError)
def test_loc_getitem_label_slice(self):
# label slices (with ints)
# real label slices
# GH 14316
self.check_result(
"loc",
slice(1, 3),
typs=["labels", "mixed", "empty", "ts", "floats"],
fails=TypeError,
)
self.check_result(
"loc", slice("20130102", "20130104"), typs=["ts"], axes=1, fails=TypeError,
)
self.check_result(
"loc", slice(2, 8), typs=["mixed"], axes=0, fails=TypeError,
)
self.check_result(
"loc", slice(2, 8), typs=["mixed"], axes=1, fails=KeyError,
)
self.check_result(
"loc", slice(2, 4, 2), typs=["mixed"], axes=0, fails=TypeError,
)
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)
class TestLoc2:
# TODO: better name, just separating out things that rely on base class
def test_loc_getitem_dups(self):
# GH 5678
# repeated getitems on a dup index returning a ndarray
df = DataFrame(
np.random.random_sample((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 = tuple(["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 = tuple(["r", "bar"])
df = df_orig.copy()
df.loc[indexer] *= 2.0
assert df.loc[indexer] == 2.0 * df_orig.loc[indexer]
indexer = tuple(["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"]
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")
expected = DataFrame(
{"id": ["A"], "a": [1.2], "b": [0.0], "c": [-2.5]}, dtype="float32"
) # 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="with any missing labels"):
s[["a", "d"]]
s = Series(range(3))
with pytest.raises(KeyError, match="with any missing labels"):
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.random((3, 3)), index=["a", "b", "c"], columns=["e", "f", "g"]
)
# raise a KeyError?
msg = (
r"\"None of \[Int64Index\(\[1, 2\], dtype='int64'\)\] are "
r"in the \[index\]\""
)
with pytest.raises(KeyError, match=msg):
df.loc[[1, 2], [1, 2]]
# 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 = (
r"\"None of \[Int64Index\(\[-1, -2\], dtype='int64'\)\] 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[["4"]]
s.loc[-1] = 3
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[[-1, -2]]
s["a"] = 2
msg = (
r"\"None of \[Int64Index\(\[-2\], dtype='int64'\)\] 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
# inconsistency between .loc[values] and .loc[values,:]
# GH 7999
df = DataFrame([["a"], ["b"]], index=[1, 2], columns=["value"])
msg = (
r"\"None of \[Int64Index\(\[3\], dtype='int64'\)\] 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]]
with pytest.raises(
KeyError,
match=re.escape(
"\"None of [Int64Index([3], dtype='int64')] are in the [index]\""
),
):
s.loc[[3]]
# a non-match and a match
with pytest.raises(KeyError, match="with any missing labels"):
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.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.rand(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
def test_loc_setitem_consistency(self):
# 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 = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
df.loc[:, "date"] = 0
tm.assert_frame_equal(df, expected)
df = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
df.loc[:, "date"] = np.array(0, dtype=np.int64)
tm.assert_frame_equal(df, expected)
df = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
df.loc[:, "date"] = np.array([0, 0, 0, 0, 0], dtype=np.int64)
tm.assert_frame_equal(df, expected)
expected = DataFrame(
{
"date": Series("foo", index=range(5)),
"val": Series(range(5), dtype=np.int64),
}
)
df = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
df.loc[:, "date"] = "foo"
tm.assert_frame_equal(df, expected)
expected = DataFrame(
{
"date": Series(1.0, index=range(5)),
"val": Series(range(5), dtype=np.int64),
}
)
df = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
df.loc[:, "date"] = 1.0
tm.assert_frame_equal(df, expected)
# GH 15494
# setting on frame with single row
df = DataFrame({"date": Series([Timestamp("20180101")])})
df.loc[:, "date"] = "string"
expected = DataFrame({"date": Series(["string"])})
tm.assert_frame_equal(df, expected)
def test_loc_setitem_consistency_empty(self):
# empty (essentially noops)
expected = DataFrame(columns=["x", "y"])
expected["x"] = expected["x"].astype(np.int64)
df = DataFrame(columns=["x", "y"])
df.loc[:, "x"] = 1
tm.assert_frame_equal(df, expected)
df = DataFrame(columns=["x", "y"])
df["x"] = 1
tm.assert_frame_equal(df, expected)
def test_loc_setitem_consistency_slice_column_len(self):
# .loc[:,column] setting with slice == len of the column
# GH10408
data = """Level_0,,,Respondent,Respondent,Respondent,OtherCat,OtherCat
Level_1,,,Something,StartDate,EndDate,Yes/No,SomethingElse
Region,Site,RespondentID,,,,,
Region_1,Site_1,3987227376,A,5/25/2015 10:59,5/25/2015 11:22,Yes,
Region_1,Site_1,3980680971,A,5/21/2015 9:40,5/21/2015 9:52,Yes,Yes
Region_1,Site_2,3977723249,A,5/20/2015 8:27,5/20/2015 8:41,Yes,
Region_1,Site_2,3977723089,A,5/20/2015 8:33,5/20/2015 9:09,Yes,No"""
df = pd.read_csv(StringIO(data), header=[0, 1], index_col=[0, 1, 2])
df.loc[:, ("Respondent", "StartDate")] = pd.to_datetime(
df.loc[:, ("Respondent", "StartDate")]
)
df.loc[:, ("Respondent", "EndDate")] = pd.to_datetime(
df.loc[:, ("Respondent", "EndDate")]
)
df.loc[:, ("Respondent", "Duration")] = (
df.loc[:, ("Respondent", "EndDate")]
- df.loc[:, ("Respondent", "StartDate")]
)
df.loc[:, ("Respondent", "Duration")] = df.loc[
:, ("Respondent", "Duration")
].astype("timedelta64[s]")
expected = Series(
[1380, 720, 840, 2160.0], index=df.index, name=("Respondent", "Duration")
)
tm.assert_series_equal(df[("Respondent", "Duration")], expected)
@pytest.mark.parametrize("unit", ["Y", "M", "D", "h", "m", "s", "ms", "us"])
def test_loc_assign_non_ns_datetime(self, unit):
# GH 27395, non-ns dtype assignment via .loc should work
# and return the same result when using simple assignment
df = DataFrame(
{
"timestamp": [
np.datetime64("2017-02-11 12:41:29"),
np.datetime64("1991-11-07 04:22:37"),
]
}
)
df.loc[:, unit] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]")
df["expected"] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]")
expected = Series(df.loc[:, "expected"], name=unit)
tm.assert_series_equal(df.loc[:, unit], expected)
def test_loc_modify_datetime(self):
# see gh-28837
df = DataFrame.from_dict(
{"date": [1485264372711, 1485265925110, 1540215845888, 1540282121025]}
)
df["date_dt"] = pd.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(pd.to_datetime)
tm.assert_frame_equal(df, expected)
def test_loc_setitem_frame(self):
df = DataFrame(np.random.randn(4, 4), index=list("abcd"), columns=list("ABCD"))
result = df.iloc[0, 0]
df.loc["a", "A"] = 1
result = df.loc["a", "A"]
assert result == 1
result = df.iloc[0, 0]
assert result == 1
df.loc[:, "B":"D"] = 0
expected = df.loc[:, "B":"D"]
result = df.iloc[:, 1:]
tm.assert_frame_equal(result, expected)
# GH 6254
# setting issue
df = DataFrame(index=[3, 5, 4], columns=["A"])
df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64")
expected = DataFrame(dict(A=Series([1, 2, 3], index=[4, 3, 5]))).reindex(
index=[3, 5, 4]
)
tm.assert_frame_equal(df, expected)
# 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
expected = DataFrame(
dict(A=Series(val1, index=keys1), B=Series(val2, index=keys2))
).reindex(index=index)
tm.assert_frame_equal(df, expected)
# 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)
# 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=[1, 2], dtype=object, name=0)
tm.assert_series_equal(result, expected)
expected = DataFrame({1: [5, 2], 2: [6, 4], "a": ["a", "b"]})
df.loc[0, [1, 2]] = [5, 6]
tm.assert_frame_equal(df, expected)
def test_loc_setitem_frame_multiples(self):
# multiple setting
df = DataFrame(
{"A": ["foo", "bar", "baz"], "B": Series(range(3), dtype=np.int64)}
)
rhs = df.loc[1:2]
rhs.index = df.index[0:2]
df.loc[0:1] = rhs
expected = DataFrame(
{"A": ["bar", "baz", "baz"], "B": Series([1, 2, 2], dtype=np.int64)}
)
tm.assert_frame_equal(df, expected)
# multiple setting with frame on rhs (with M8)
df = DataFrame(
{
"date": date_range("2000-01-01", "2000-01-5"),
"val": Series(range(5), dtype=np.int64),
}
)
expected = DataFrame(
{
"date": [
Timestamp("20000101"),
Timestamp("20000102"),
Timestamp("20000101"),
Timestamp("20000102"),
Timestamp("20000103"),
],
"val": Series([0, 1, 0, 1, 2], dtype=np.int64),
}
)
rhs = df.loc[0:2]
rhs.index = df.index[2:5]
df.loc[2:4] = rhs
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize(
"indexer", [["A"], slice(None, "A", None), np.array(["A"])]
)
@pytest.mark.parametrize("value", [["Z"], np.array(["Z"])])
def test_loc_setitem_with_scalar_index(self, indexer, value):
# GH #19474
# assigning like "df.loc[0, ['A']] = ['Z']" should be evaluated
# elementwisely, not using "setter('A', ['Z'])".
df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
df.loc[0, indexer] = value
result = df.loc[0, "A"]
assert is_scalar(result) and result == "Z"
@pytest.mark.parametrize(
"index,box,expected",
[
(
([0, 2], ["A", "B", "C", "D"]),
7,
pd.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],
pd.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),
pd.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]],
pd.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),
pd.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"]),
pd.DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]),
pd.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 = pd.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):
# 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)
# 12045
import datetime
df = DataFrame(
{"date": [datetime.datetime(2012, 1, 1), datetime.datetime(1012, 1, 2)]}
)
expected = df.dtypes
result = df.iloc[[0]]
tm.assert_series_equal(result.dtypes, expected)
result = df.iloc[[1]]
tm.assert_series_equal(result.dtypes, expected)
# 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):
# GH#12862 should not raise on assigning the second value
vals = [
pd.to_datetime(42).tz_localize("UTC"),
pd.to_datetime(666).tz_localize("UTC"),
]
expected = pd.Series(vals, index=["foo", "bar"])
ser = pd.Series(dtype=object)
ser["foo"] = vals[0]
ser["bar"] = vals[1]
tm.assert_series_equal(ser, expected)
ser = pd.Series(dtype=object)
ser.loc["foo"] = vals[0]
ser.loc["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)
def test_loc_non_unique_memory_error(self):
# GH 4280
# non_unique index with a large selection triggers a memory error
columns = list("ABCDEFG")
def gen_test(l, l2):
return pd.concat(
[
DataFrame(
np.random.randn(l, len(columns)),
index=np.arange(l),
columns=columns,
),
DataFrame(
np.ones((l2, len(columns))), index=[0] * l2, columns=columns
),
]
)
def gen_expected(df, mask):
len_mask = len(mask)
return pd.concat(
[
df.take([0]),
DataFrame(
np.ones((len_mask, len(columns))),
index=[0] * len_mask,
columns=columns,
),
df.take(mask[1:]),
]
)
df = gen_test(900, 100)
assert df.index.is_unique is False
mask = np.arange(100)
result = df.loc[mask]
expected = gen_expected(df, mask)
tm.assert_frame_equal(result, expected)
df = gen_test(900000, 100000)
assert df.index.is_unique is False
mask = np.arange(100000)
result = df.loc[mask]
expected = gen_expected(df, mask)
tm.assert_frame_equal(result, expected)
def test_loc_name(self):
# GH 3880
df = DataFrame([[1, 1], [1, 1]])
df.index.name = "index_name"
result = df.iloc[[0, 1]].index.name
assert result == "index_name"
result = df.loc[[0, 1]].index.name
assert result == "index_name"
def test_loc_empty_list_indexer_is_ok(self):
df = tm.makeCustomDataframe(5, 2)
# vertical empty
tm.assert_frame_equal(
df.loc[:, []], df.iloc[:, :0], check_index_type=True, check_column_type=True
)
# horizontal empty
tm.assert_frame_equal(
df.loc[[], :], df.iloc[:0, :], check_index_type=True, check_column_type=True
)
# horizontal empty
tm.assert_frame_equal(
df.loc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True
)
def test_identity_slice_returns_new_object(self):
# 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
# should be a shallow copy
original_df["a"] = [4, 4, 4]
assert (sliced_df["a"] == 4).all()
# These should not return copies
assert original_df is original_df.loc[:, :]
df = DataFrame(np.random.randn(10, 4))
assert df[0] is df.loc[:, 0]
# Same tests for Series
original_series = Series([1, 2, 3, 4, 5, 6])
sliced_series = original_series.loc[:]
assert sliced_series is not original_series
assert original_series[:] is not original_series
original_series[:3] = [7, 8, 9]
assert all(sliced_series[:3] == [7, 8, 9])
@pytest.mark.xfail(reason="accidental fix reverted - GH37497")
def test_loc_copy_vs_view(self):
# GH 15631
x = DataFrame(zip(range(3), range(3)), columns=["a", "b"])
y = x.copy()
q = y.loc[:, "a"]
q += 2
tm.assert_frame_equal(x, y)
z = x.copy()
q = z.loc[x.index, "a"]
q += 2
tm.assert_frame_equal(x, z)
def test_loc_uint64(self):
# GH20722
# Test whether loc accept uint64 max value as index.
s = pd.Series(
[1, 2], index=[np.iinfo("uint64").max - 1, np.iinfo("uint64").max]
)
result = s.loc[np.iinfo("uint64").max - 1]
expected = s.iloc[0]
assert result == expected
result = s.loc[[np.iinfo("uint64").max - 1]]
expected = s.iloc[[0]]
tm.assert_series_equal(result, expected)
result = s.loc[[np.iinfo("uint64").max - 1, np.iinfo("uint64").max]]
tm.assert_series_equal(result, s)
def test_loc_setitem_empty_append(self):
# GH6173, various appends to an empty dataframe
data = [1, 2, 3]
expected = DataFrame({"x": data, "y": [None] * len(data)})
# appends to fit length of data
df = DataFrame(columns=["x", "y"])
df.loc[:, "x"] = data
tm.assert_frame_equal(df, expected)
# 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)
@pytest.mark.xfail(_is_numpy_dev, reason="gh-35481")
def test_loc_setitem_empty_append_raises(self):
# GH6173, various appends to an empty dataframe
data = [1, 2]
df = DataFrame(columns=["x", "y"])
df.index = df.index.astype(np.int64)
msg = (
r"None of \[Int64Index\(\[0, 1\], dtype='int64'\)\] "
r"are in the \[index\]"
)
with pytest.raises(KeyError, match=msg):
df.loc[[0, 1], "x"] = data
msg = "cannot copy sequence with size 2 to array axis with dimension 0"
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 = pd.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 = pd.Series(index=range(2010, 2020), dtype=np.float64)
result.loc[2015:2010:-1] = [6, 5, 4, 3, 2, 1]
tm.assert_series_equal(result, expected)
def test_series_loc_getitem_label_list_missing_values():
# gh-11428
key = np.array(
["2001-01-04", "2001-01-02", "2001-01-04", "2001-01-14"], dtype="datetime64"
)
s = Series([2, 5, 8, 11], date_range("2001-01-01", freq="D", periods=4))
with pytest.raises(KeyError, match="with any missing labels"):
s.loc[key]
@pytest.mark.parametrize(
"columns, column_key, expected_columns, check_column_type",
[
([2011, 2012, 2013], [2011, 2012], [0, 1], True),
([2011, 2012, "All"], [2011, 2012], [0, 1], False),
([2011, 2012, "All"], [2011, "All"], [0, 2], True),
],
)
def test_loc_getitem_label_list_integer_labels(
columns, column_key, expected_columns, check_column_type
):
# gh-14836
df = DataFrame(np.random.rand(3, 3), columns=columns, index=list("ABC"))
expected = df.iloc[:, expected_columns]
result = df.loc[["A", "B", "C"], column_key]
tm.assert_frame_equal(result, expected, check_column_type=check_column_type)
def test_loc_setitem_float_intindex():
# GH 8720
rand_data = np.random.randn(8, 4)
result = pd.DataFrame(rand_data)
result.loc[:, 0.5] = np.nan
expected_data = np.hstack((rand_data, np.array([np.nan] * 8).reshape(8, 1)))
expected = pd.DataFrame(expected_data, columns=[0.0, 1.0, 2.0, 3.0, 0.5])
tm.assert_frame_equal(result, expected)
result = pd.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 = pd.DataFrame(
np.ones((10, 8)),
index=tuple("ABCDEFGHIJ"),
columns=pd.MultiIndex.from_tuples(cols),
)
result = df.loc(axis=1)[(2014, 9):(2015, 8)]
expected = pd.DataFrame(
np.ones((10, 4)),
index=tuple("ABCDEFGHIJ"),
columns=pd.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 = pd.DataFrame(
"a", index=range(2), columns=pd.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 = pd.Series(range(2), pd.Index([1, 2.0], dtype=object))
result = ser.loc[1]
assert result == 0
def test_loc_with_positional_slice_deprecation():
# GH#31840
ser = pd.Series(range(4), index=["A", "B", "C", "D"])
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
ser.loc[:3] = 2
expected = pd.Series([2, 2, 2, 3], index=["A", "B", "C", "D"])
tm.assert_series_equal(ser, expected)
def test_loc_slice_disallows_positional():
# GH#16121, GH#24612, GH#31810
dti = pd.date_range("2016-01-01", periods=3)
df = pd.DataFrame(np.random.random((3, 2)), index=dti)
ser = df[0]
msg = (
"cannot do slice indexing on DatetimeIndex with these "
r"indexers \[1\] of type int"
)
for obj in [df, ser]:
with pytest.raises(TypeError, match=msg):
obj.loc[1:3]
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# GH#31840 deprecated incorrect behavior
obj.loc[1:3] = 1
with pytest.raises(TypeError, match=msg):
df.loc[1:3, 1]
with tm.assert_produces_warning(FutureWarning):
# GH#31840 deprecated incorrect behavior
df.loc[1:3, 1] = 2
def test_loc_datetimelike_mismatched_dtypes():
# GH#32650 dont mix and match datetime/timedelta/period dtypes
df = pd.DataFrame(
np.random.randn(5, 3),
columns=["a", "b", "c"],
index=pd.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 = pd.DataFrame(np.random.randn(24, 10), index=idx)
tm.assert_frame_equal(df, df.loc[idx])
tm.assert_frame_equal(df, df.loc[list(idx)])
tm.assert_frame_equal(df, df.loc[list(idx)])
tm.assert_frame_equal(df.iloc[0:5], df.loc[idx[0:5]])
tm.assert_frame_equal(df, df.loc[list(idx)])