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""" test scalar indexing, including at and iat """
from datetime import (
datetime,
timedelta,
)
import itertools
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
Timedelta,
Timestamp,
date_range,
)
import pandas._testing as tm
def generate_indices(f, values=False):
"""
generate the indices
if values is True , use the axis values
is False, use the range
"""
axes = f.axes
if values:
axes = (list(range(len(ax))) for ax in axes)
return itertools.product(*axes)
class TestScalar:
@pytest.mark.parametrize("kind", ["series", "frame"])
@pytest.mark.parametrize("col", ["ints", "uints"])
def test_iat_set_ints(self, kind, col, request):
f = request.getfixturevalue(f"{kind}_{col}")
indices = generate_indices(f, True)
for i in indices:
f.iat[i] = 1
expected = f.values[i]
tm.assert_almost_equal(expected, 1)
@pytest.mark.parametrize("kind", ["series", "frame"])
@pytest.mark.parametrize("col", ["labels", "ts", "floats"])
def test_iat_set_other(self, kind, col, request):
f = request.getfixturevalue(f"{kind}_{col}")
msg = "iAt based indexing can only have integer indexers"
with pytest.raises(ValueError, match=msg):
idx = next(generate_indices(f, False))
f.iat[idx] = 1
@pytest.mark.parametrize("kind", ["series", "frame"])
@pytest.mark.parametrize("col", ["ints", "uints", "labels", "ts", "floats"])
def test_at_set_ints_other(self, kind, col, request):
f = request.getfixturevalue(f"{kind}_{col}")
indices = generate_indices(f, False)
for i in indices:
f.at[i] = 1
expected = f.loc[i]
tm.assert_almost_equal(expected, 1)
class TestAtAndiAT:
# at and iat tests that don't need Base class
def test_float_index_at_iat(self):
ser = Series([1, 2, 3], index=[0.1, 0.2, 0.3])
for el, item in ser.items():
assert ser.at[el] == item
for i in range(len(ser)):
assert ser.iat[i] == i + 1
def test_at_iat_coercion(self):
# as timestamp is not a tuple!
dates = date_range("1/1/2000", periods=8)
df = DataFrame(
np.random.default_rng(2).standard_normal((8, 4)),
index=dates,
columns=["A", "B", "C", "D"],
)
s = df["A"]
result = s.at[dates[5]]
xp = s.values[5]
assert result == xp
@pytest.mark.parametrize(
"ser, expected",
[
[
Series(["2014-01-01", "2014-02-02"], dtype="datetime64[ns]"),
Timestamp("2014-02-02"),
],
[
Series(["1 days", "2 days"], dtype="timedelta64[ns]"),
Timedelta("2 days"),
],
],
)
def test_iloc_iat_coercion_datelike(self, indexer_ial, ser, expected):
# GH 7729
# make sure we are boxing the returns
result = indexer_ial(ser)[1]
assert result == expected
def test_imethods_with_dups(self):
# GH6493
# iat/iloc with dups
s = Series(range(5), index=[1, 1, 2, 2, 3], dtype="int64")
result = s.iloc[2]
assert result == 2
result = s.iat[2]
assert result == 2
msg = "index 10 is out of bounds for axis 0 with size 5"
with pytest.raises(IndexError, match=msg):
s.iat[10]
msg = "index -10 is out of bounds for axis 0 with size 5"
with pytest.raises(IndexError, match=msg):
s.iat[-10]
result = s.iloc[[2, 3]]
expected = Series([2, 3], [2, 2], dtype="int64")
tm.assert_series_equal(result, expected)
df = s.to_frame()
result = df.iloc[2]
expected = Series(2, index=[0], name=2)
tm.assert_series_equal(result, expected)
result = df.iat[2, 0]
assert result == 2
def test_frame_at_with_duplicate_axes(self):
# GH#33041
arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2)
df = DataFrame(arr, columns=["A", "A"])
result = df.at[0, "A"]
expected = df.iloc[0].copy()
tm.assert_series_equal(result, expected)
result = df.T.at["A", 0]
tm.assert_series_equal(result, expected)
# setter
df.at[1, "A"] = 2
expected = Series([2.0, 2.0], index=["A", "A"], name=1)
tm.assert_series_equal(df.iloc[1], expected)
def test_at_getitem_dt64tz_values(self):
# gh-15822
df = DataFrame(
{
"name": ["John", "Anderson"],
"date": [
Timestamp(2017, 3, 13, 13, 32, 56),
Timestamp(2017, 2, 16, 12, 10, 3),
],
}
)
df["date"] = df["date"].dt.tz_localize("Asia/Shanghai")
expected = Timestamp("2017-03-13 13:32:56+0800", tz="Asia/Shanghai")
result = df.loc[0, "date"]
assert result == expected
result = df.at[0, "date"]
assert result == expected
def test_mixed_index_at_iat_loc_iloc_series(self):
# GH 19860
s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
for el, item in s.items():
assert s.at[el] == s.loc[el] == item
for i in range(len(s)):
assert s.iat[i] == s.iloc[i] == i + 1
with pytest.raises(KeyError, match="^4$"):
s.at[4]
with pytest.raises(KeyError, match="^4$"):
s.loc[4]
def test_mixed_index_at_iat_loc_iloc_dataframe(self):
# GH 19860
df = DataFrame(
[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], columns=["a", "b", "c", 1, 2]
)
for rowIdx, row in df.iterrows():
for el, item in row.items():
assert df.at[rowIdx, el] == df.loc[rowIdx, el] == item
for row in range(2):
for i in range(5):
assert df.iat[row, i] == df.iloc[row, i] == row * 5 + i
with pytest.raises(KeyError, match="^3$"):
df.at[0, 3]
with pytest.raises(KeyError, match="^3$"):
df.loc[0, 3]
def test_iat_setter_incompatible_assignment(self):
# GH 23236
result = DataFrame({"a": [0.0, 1.0], "b": [4, 5]})
result.iat[0, 0] = None
expected = DataFrame({"a": [None, 1], "b": [4, 5]})
tm.assert_frame_equal(result, expected)
def test_iat_dont_wrap_object_datetimelike():
# GH#32809 .iat calls go through DataFrame._get_value, should not
# call maybe_box_datetimelike
dti = date_range("2016-01-01", periods=3)
tdi = dti - dti
ser = Series(dti.to_pydatetime(), dtype=object)
ser2 = Series(tdi.to_pytimedelta(), dtype=object)
df = DataFrame({"A": ser, "B": ser2})
assert (df.dtypes == object).all()
for result in [df.at[0, "A"], df.iat[0, 0], df.loc[0, "A"], df.iloc[0, 0]]:
assert result is ser[0]
assert isinstance(result, datetime)
assert not isinstance(result, Timestamp)
for result in [df.at[1, "B"], df.iat[1, 1], df.loc[1, "B"], df.iloc[1, 1]]:
assert result is ser2[1]
assert isinstance(result, timedelta)
assert not isinstance(result, Timedelta)
def test_at_with_tuple_index_get():
# GH 26989
# DataFrame.at getter works with Index of tuples
df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
assert df.index.nlevels == 1
assert df.at[(1, 2), "a"] == 1
# Series.at getter works with Index of tuples
series = df["a"]
assert series.index.nlevels == 1
assert series.at[(1, 2)] == 1
@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
def test_at_with_tuple_index_set():
# GH 26989
# DataFrame.at setter works with Index of tuples
df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)])
assert df.index.nlevels == 1
df.at[(1, 2), "a"] = 2
assert df.at[(1, 2), "a"] == 2
# Series.at setter works with Index of tuples
series = df["a"]
assert series.index.nlevels == 1
series.at[1, 2] = 3
assert series.at[1, 2] == 3
class TestMultiIndexScalar:
def test_multiindex_at_get(self):
# GH 26989
# DataFrame.at and DataFrame.loc getter works with MultiIndex
df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
assert df.index.nlevels == 2
assert df.at[(1, 3), "a"] == 1
assert df.loc[(1, 3), "a"] == 1
# Series.at and Series.loc getter works with MultiIndex
series = df["a"]
assert series.index.nlevels == 2
assert series.at[1, 3] == 1
assert series.loc[1, 3] == 1
@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
def test_multiindex_at_set(self):
# GH 26989
# DataFrame.at and DataFrame.loc setter works with MultiIndex
df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]])
assert df.index.nlevels == 2
df.at[(1, 3), "a"] = 3
assert df.at[(1, 3), "a"] == 3
df.loc[(1, 3), "a"] = 4
assert df.loc[(1, 3), "a"] == 4
# Series.at and Series.loc setter works with MultiIndex
series = df["a"]
assert series.index.nlevels == 2
series.at[1, 3] = 5
assert series.at[1, 3] == 5
series.loc[1, 3] = 6
assert series.loc[1, 3] == 6
def test_multiindex_at_get_one_level(self):
# GH#38053
s2 = Series((0, 1), index=[[False, True]])
result = s2.at[False]
assert result == 0
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