251 lines
7.7 KiB
Python
251 lines
7.7 KiB
Python
from datetime import (
|
|
datetime,
|
|
timezone,
|
|
)
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.errors import InvalidIndexError
|
|
|
|
from pandas import (
|
|
CategoricalDtype,
|
|
CategoricalIndex,
|
|
DataFrame,
|
|
DatetimeIndex,
|
|
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)]})
|
|
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])
|
|
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):
|
|
df = DataFrame(
|
|
data=[[1] * 2], index=DatetimeIndex(data=["2019-01-01", "2019-01-02"])
|
|
)
|
|
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.randn(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
|