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from datetime import (
datetime,
timezone,
)
import numpy as np
import pytest
from pandas.errors import InvalidIndexError
from pandas import (
CategoricalDtype,
CategoricalIndex,
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
Timestamp,
)
import pandas._testing as tm
def test_at_timezone():
# https://github.com/pandas-dev/pandas/issues/33544
result = DataFrame({"foo": [datetime(2000, 1, 1)]})
with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
result.at[0, "foo"] = datetime(2000, 1, 2, tzinfo=timezone.utc)
expected = DataFrame(
{"foo": [datetime(2000, 1, 2, tzinfo=timezone.utc)]}, dtype=object
)
tm.assert_frame_equal(result, expected)
def test_selection_methods_of_assigned_col():
# GH 29282
df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
df2 = DataFrame(data={"c": [7, 8, 9]}, index=[2, 1, 0])
df["c"] = df2["c"]
df.at[1, "c"] = 11
result = df
expected = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [9, 11, 7]})
tm.assert_frame_equal(result, expected)
result = df.at[1, "c"]
assert result == 11
result = df["c"]
expected = Series([9, 11, 7], name="c")
tm.assert_series_equal(result, expected)
result = df[["c"]]
expected = DataFrame({"c": [9, 11, 7]})
tm.assert_frame_equal(result, expected)
class TestAtSetItem:
def test_at_setitem_item_cache_cleared(self):
# GH#22372 Note the multi-step construction is necessary to trigger
# the original bug. pandas/issues/22372#issuecomment-413345309
df = DataFrame(index=[0])
df["x"] = 1
df["cost"] = 2
# accessing df["cost"] adds "cost" to the _item_cache
df["cost"]
# This loc[[0]] lookup used to call _consolidate_inplace at the
# BlockManager level, which failed to clear the _item_cache
df.loc[[0]]
df.at[0, "x"] = 4
df.at[0, "cost"] = 789
expected = DataFrame(
{"x": [4], "cost": 789},
index=[0],
columns=Index(["x", "cost"], dtype=object),
)
tm.assert_frame_equal(df, expected)
# And in particular, check that the _item_cache has updated correctly.
tm.assert_series_equal(df["cost"], expected["cost"])
def test_at_setitem_mixed_index_assignment(self):
# GH#19860
ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
ser.at["a"] = 11
assert ser.iat[0] == 11
ser.at[1] = 22
assert ser.iat[3] == 22
def test_at_setitem_categorical_missing(self):
df = DataFrame(
index=range(3), columns=range(3), dtype=CategoricalDtype(["foo", "bar"])
)
df.at[1, 1] = "foo"
expected = DataFrame(
[
[np.nan, np.nan, np.nan],
[np.nan, "foo", np.nan],
[np.nan, np.nan, np.nan],
],
dtype=CategoricalDtype(["foo", "bar"]),
)
tm.assert_frame_equal(df, expected)
def test_at_setitem_multiindex(self):
df = DataFrame(
np.zeros((3, 2), dtype="int64"),
columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]),
)
df.at[0, "a"] = 10
expected = DataFrame(
[[10, 10], [0, 0], [0, 0]],
columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]),
)
tm.assert_frame_equal(df, expected)
@pytest.mark.parametrize("row", (Timestamp("2019-01-01"), "2019-01-01"))
def test_at_datetime_index(self, row):
# Set float64 dtype to avoid upcast when setting .5
df = DataFrame(
data=[[1] * 2], index=DatetimeIndex(data=["2019-01-01", "2019-01-02"])
).astype({0: "float64"})
expected = DataFrame(
data=[[0.5, 1], [1.0, 1]],
index=DatetimeIndex(data=["2019-01-01", "2019-01-02"]),
)
df.at[row, 0] = 0.5
tm.assert_frame_equal(df, expected)
class TestAtSetItemWithExpansion:
def test_at_setitem_expansion_series_dt64tz_value(self, tz_naive_fixture):
# GH#25506
ts = Timestamp("2017-08-05 00:00:00+0100", tz=tz_naive_fixture)
result = Series(ts)
result.at[1] = ts
expected = Series([ts, ts])
tm.assert_series_equal(result, expected)
class TestAtWithDuplicates:
def test_at_with_duplicate_axes_requires_scalar_lookup(self):
# GH#33041 check that falling back to loc doesn't allow non-scalar
# args to slip in
arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2)
df = DataFrame(arr, columns=["A", "A"])
msg = "Invalid call for scalar access"
with pytest.raises(ValueError, match=msg):
df.at[[1, 2]]
with pytest.raises(ValueError, match=msg):
df.at[1, ["A"]]
with pytest.raises(ValueError, match=msg):
df.at[:, "A"]
with pytest.raises(ValueError, match=msg):
df.at[[1, 2]] = 1
with pytest.raises(ValueError, match=msg):
df.at[1, ["A"]] = 1
with pytest.raises(ValueError, match=msg):
df.at[:, "A"] = 1
class TestAtErrors:
# TODO: De-duplicate/parametrize
# test_at_series_raises_key_error2, test_at_frame_raises_key_error2
def test_at_series_raises_key_error(self, indexer_al):
# GH#31724 .at should match .loc
ser = Series([1, 2, 3], index=[3, 2, 1])
result = indexer_al(ser)[1]
assert result == 3
with pytest.raises(KeyError, match="a"):
indexer_al(ser)["a"]
def test_at_frame_raises_key_error(self, indexer_al):
# GH#31724 .at should match .loc
df = DataFrame({0: [1, 2, 3]}, index=[3, 2, 1])
result = indexer_al(df)[1, 0]
assert result == 3
with pytest.raises(KeyError, match="a"):
indexer_al(df)["a", 0]
with pytest.raises(KeyError, match="a"):
indexer_al(df)[1, "a"]
def test_at_series_raises_key_error2(self, indexer_al):
# at should not fallback
# GH#7814
# GH#31724 .at should match .loc
ser = Series([1, 2, 3], index=list("abc"))
result = indexer_al(ser)["a"]
assert result == 1
with pytest.raises(KeyError, match="^0$"):
indexer_al(ser)[0]
def test_at_frame_raises_key_error2(self, indexer_al):
# GH#31724 .at should match .loc
df = DataFrame({"A": [1, 2, 3]}, index=list("abc"))
result = indexer_al(df)["a", "A"]
assert result == 1
with pytest.raises(KeyError, match="^0$"):
indexer_al(df)["a", 0]
def test_at_frame_multiple_columns(self):
# GH#48296 - at shouldn't modify multiple columns
df = DataFrame({"a": [1, 2], "b": [3, 4]})
new_row = [6, 7]
with pytest.raises(
InvalidIndexError,
match=f"You can only assign a scalar value not a \\{type(new_row)}",
):
df.at[5] = new_row
def test_at_getitem_mixed_index_no_fallback(self):
# GH#19860
ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
with pytest.raises(KeyError, match="^0$"):
ser.at[0]
with pytest.raises(KeyError, match="^4$"):
ser.at[4]
def test_at_categorical_integers(self):
# CategoricalIndex with integer categories that don't happen to match
# the Categorical's codes
ci = CategoricalIndex([3, 4])
arr = np.arange(4).reshape(2, 2)
frame = DataFrame(arr, index=ci)
for df in [frame, frame.T]:
for key in [0, 1]:
with pytest.raises(KeyError, match=str(key)):
df.at[key, key]
def test_at_applied_for_rows(self):
# GH#48729 .at should raise InvalidIndexError when assigning rows
df = DataFrame(index=["a"], columns=["col1", "col2"])
new_row = [123, 15]
with pytest.raises(
InvalidIndexError,
match=f"You can only assign a scalar value not a \\{type(new_row)}",
):
df.at["a"] = new_row
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