done
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252
lib/python3.11/site-packages/pandas/tests/indexing/test_at.py
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252
lib/python3.11/site-packages/pandas/tests/indexing/test_at.py
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from datetime import (
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datetime,
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timezone,
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)
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import numpy as np
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import pytest
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from pandas.errors import InvalidIndexError
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from pandas import (
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CategoricalDtype,
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CategoricalIndex,
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DataFrame,
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DatetimeIndex,
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MultiIndex,
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Series,
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Timestamp,
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)
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import pandas._testing as tm
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def test_at_timezone():
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# https://github.com/pandas-dev/pandas/issues/33544
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result = DataFrame({"foo": [datetime(2000, 1, 1)]})
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with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"):
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result.at[0, "foo"] = datetime(2000, 1, 2, tzinfo=timezone.utc)
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expected = DataFrame(
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{"foo": [datetime(2000, 1, 2, tzinfo=timezone.utc)]}, dtype=object
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)
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tm.assert_frame_equal(result, expected)
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def test_selection_methods_of_assigned_col():
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# GH 29282
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df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]})
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df2 = DataFrame(data={"c": [7, 8, 9]}, index=[2, 1, 0])
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df["c"] = df2["c"]
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df.at[1, "c"] = 11
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result = df
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expected = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [9, 11, 7]})
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tm.assert_frame_equal(result, expected)
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result = df.at[1, "c"]
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assert result == 11
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result = df["c"]
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expected = Series([9, 11, 7], name="c")
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tm.assert_series_equal(result, expected)
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result = df[["c"]]
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expected = DataFrame({"c": [9, 11, 7]})
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tm.assert_frame_equal(result, expected)
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class TestAtSetItem:
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def test_at_setitem_item_cache_cleared(self):
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# GH#22372 Note the multi-step construction is necessary to trigger
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# the original bug. pandas/issues/22372#issuecomment-413345309
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df = DataFrame(index=[0])
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df["x"] = 1
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df["cost"] = 2
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# accessing df["cost"] adds "cost" to the _item_cache
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df["cost"]
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# This loc[[0]] lookup used to call _consolidate_inplace at the
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# BlockManager level, which failed to clear the _item_cache
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df.loc[[0]]
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df.at[0, "x"] = 4
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df.at[0, "cost"] = 789
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expected = DataFrame({"x": [4], "cost": 789}, index=[0])
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tm.assert_frame_equal(df, expected)
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# And in particular, check that the _item_cache has updated correctly.
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tm.assert_series_equal(df["cost"], expected["cost"])
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def test_at_setitem_mixed_index_assignment(self):
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# GH#19860
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ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
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ser.at["a"] = 11
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assert ser.iat[0] == 11
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ser.at[1] = 22
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assert ser.iat[3] == 22
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def test_at_setitem_categorical_missing(self):
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df = DataFrame(
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index=range(3), columns=range(3), dtype=CategoricalDtype(["foo", "bar"])
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)
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df.at[1, 1] = "foo"
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expected = DataFrame(
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[
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[np.nan, np.nan, np.nan],
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[np.nan, "foo", np.nan],
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[np.nan, np.nan, np.nan],
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],
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dtype=CategoricalDtype(["foo", "bar"]),
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)
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tm.assert_frame_equal(df, expected)
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def test_at_setitem_multiindex(self):
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df = DataFrame(
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np.zeros((3, 2), dtype="int64"),
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columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]),
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)
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df.at[0, "a"] = 10
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expected = DataFrame(
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[[10, 10], [0, 0], [0, 0]],
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columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]),
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)
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize("row", (Timestamp("2019-01-01"), "2019-01-01"))
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def test_at_datetime_index(self, row):
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# Set float64 dtype to avoid upcast when setting .5
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df = DataFrame(
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data=[[1] * 2], index=DatetimeIndex(data=["2019-01-01", "2019-01-02"])
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).astype({0: "float64"})
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expected = DataFrame(
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data=[[0.5, 1], [1.0, 1]],
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index=DatetimeIndex(data=["2019-01-01", "2019-01-02"]),
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)
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df.at[row, 0] = 0.5
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tm.assert_frame_equal(df, expected)
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class TestAtSetItemWithExpansion:
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def test_at_setitem_expansion_series_dt64tz_value(self, tz_naive_fixture):
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# GH#25506
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ts = Timestamp("2017-08-05 00:00:00+0100", tz=tz_naive_fixture)
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result = Series(ts)
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result.at[1] = ts
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expected = Series([ts, ts])
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tm.assert_series_equal(result, expected)
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class TestAtWithDuplicates:
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def test_at_with_duplicate_axes_requires_scalar_lookup(self):
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# GH#33041 check that falling back to loc doesn't allow non-scalar
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# args to slip in
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arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2)
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df = DataFrame(arr, columns=["A", "A"])
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msg = "Invalid call for scalar access"
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with pytest.raises(ValueError, match=msg):
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df.at[[1, 2]]
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with pytest.raises(ValueError, match=msg):
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df.at[1, ["A"]]
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with pytest.raises(ValueError, match=msg):
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df.at[:, "A"]
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with pytest.raises(ValueError, match=msg):
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df.at[[1, 2]] = 1
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with pytest.raises(ValueError, match=msg):
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df.at[1, ["A"]] = 1
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with pytest.raises(ValueError, match=msg):
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df.at[:, "A"] = 1
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class TestAtErrors:
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# TODO: De-duplicate/parametrize
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# test_at_series_raises_key_error2, test_at_frame_raises_key_error2
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def test_at_series_raises_key_error(self, indexer_al):
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# GH#31724 .at should match .loc
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ser = Series([1, 2, 3], index=[3, 2, 1])
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result = indexer_al(ser)[1]
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assert result == 3
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with pytest.raises(KeyError, match="a"):
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indexer_al(ser)["a"]
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def test_at_frame_raises_key_error(self, indexer_al):
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# GH#31724 .at should match .loc
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df = DataFrame({0: [1, 2, 3]}, index=[3, 2, 1])
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result = indexer_al(df)[1, 0]
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assert result == 3
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with pytest.raises(KeyError, match="a"):
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indexer_al(df)["a", 0]
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with pytest.raises(KeyError, match="a"):
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indexer_al(df)[1, "a"]
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def test_at_series_raises_key_error2(self, indexer_al):
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# at should not fallback
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# GH#7814
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# GH#31724 .at should match .loc
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ser = Series([1, 2, 3], index=list("abc"))
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result = indexer_al(ser)["a"]
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assert result == 1
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with pytest.raises(KeyError, match="^0$"):
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indexer_al(ser)[0]
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def test_at_frame_raises_key_error2(self, indexer_al):
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# GH#31724 .at should match .loc
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df = DataFrame({"A": [1, 2, 3]}, index=list("abc"))
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result = indexer_al(df)["a", "A"]
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assert result == 1
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with pytest.raises(KeyError, match="^0$"):
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indexer_al(df)["a", 0]
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def test_at_frame_multiple_columns(self):
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# GH#48296 - at shouldn't modify multiple columns
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df = DataFrame({"a": [1, 2], "b": [3, 4]})
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new_row = [6, 7]
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with pytest.raises(
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InvalidIndexError,
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match=f"You can only assign a scalar value not a \\{type(new_row)}",
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):
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df.at[5] = new_row
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def test_at_getitem_mixed_index_no_fallback(self):
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# GH#19860
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ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2])
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with pytest.raises(KeyError, match="^0$"):
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ser.at[0]
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with pytest.raises(KeyError, match="^4$"):
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ser.at[4]
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def test_at_categorical_integers(self):
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# CategoricalIndex with integer categories that don't happen to match
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# the Categorical's codes
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ci = CategoricalIndex([3, 4])
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arr = np.arange(4).reshape(2, 2)
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frame = DataFrame(arr, index=ci)
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for df in [frame, frame.T]:
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for key in [0, 1]:
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with pytest.raises(KeyError, match=str(key)):
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df.at[key, key]
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def test_at_applied_for_rows(self):
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# GH#48729 .at should raise InvalidIndexError when assigning rows
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df = DataFrame(index=["a"], columns=["col1", "col2"])
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new_row = [123, 15]
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with pytest.raises(
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InvalidIndexError,
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match=f"You can only assign a scalar value not a \\{type(new_row)}",
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):
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df.at["a"] = new_row
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