You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
129 lines
4.5 KiB
129 lines
4.5 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
from pandas import (
|
|
CategoricalDtype,
|
|
DataFrame,
|
|
NaT,
|
|
Series,
|
|
Timestamp,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestUpdate:
|
|
def test_update(self):
|
|
s = Series([1.5, np.nan, 3.0, 4.0, np.nan])
|
|
s2 = Series([np.nan, 3.5, np.nan, 5.0])
|
|
s.update(s2)
|
|
|
|
expected = Series([1.5, 3.5, 3.0, 5.0, np.nan])
|
|
tm.assert_series_equal(s, expected)
|
|
|
|
# GH 3217
|
|
df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
|
|
df["c"] = np.nan
|
|
|
|
df["c"].update(Series(["foo"], index=[0]))
|
|
expected = DataFrame(
|
|
[[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"]
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"other, dtype, expected",
|
|
[
|
|
# other is int
|
|
([61, 63], "int32", Series([10, 61, 12], dtype="int32")),
|
|
([61, 63], "int64", Series([10, 61, 12])),
|
|
([61, 63], float, Series([10.0, 61.0, 12.0])),
|
|
([61, 63], object, Series([10, 61, 12], dtype=object)),
|
|
# other is float, but can be cast to int
|
|
([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32")),
|
|
([61.0, 63.0], "int64", Series([10, 61, 12])),
|
|
([61.0, 63.0], float, Series([10.0, 61.0, 12.0])),
|
|
([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object)),
|
|
# others is float, cannot be cast to int
|
|
([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0])),
|
|
([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0])),
|
|
([61.1, 63.1], float, Series([10.0, 61.1, 12.0])),
|
|
([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object)),
|
|
# other is object, cannot be cast
|
|
([(61,), (63,)], "int32", Series([10, (61,), 12])),
|
|
([(61,), (63,)], "int64", Series([10, (61,), 12])),
|
|
([(61,), (63,)], float, Series([10.0, (61,), 12.0])),
|
|
([(61,), (63,)], object, Series([10, (61,), 12])),
|
|
],
|
|
)
|
|
def test_update_dtypes(self, other, dtype, expected):
|
|
|
|
ser = Series([10, 11, 12], dtype=dtype)
|
|
other = Series(other, index=[1, 3])
|
|
ser.update(other)
|
|
|
|
tm.assert_series_equal(ser, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"series, other, expected",
|
|
[
|
|
# update by key
|
|
(
|
|
Series({"a": 1, "b": 2, "c": 3, "d": 4}),
|
|
{"b": 5, "c": np.nan},
|
|
Series({"a": 1, "b": 5, "c": 3, "d": 4}),
|
|
),
|
|
# update by position
|
|
(Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])),
|
|
],
|
|
)
|
|
def test_update_from_non_series(self, series, other, expected):
|
|
# GH 33215
|
|
series.update(other)
|
|
tm.assert_series_equal(series, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"data, other, expected, dtype",
|
|
[
|
|
(["a", None], [None, "b"], ["a", "b"], "string[python]"),
|
|
pytest.param(
|
|
["a", None],
|
|
[None, "b"],
|
|
["a", "b"],
|
|
"string[pyarrow]",
|
|
marks=td.skip_if_no("pyarrow", min_version="1.0.0"),
|
|
),
|
|
([1, None], [None, 2], [1, 2], "Int64"),
|
|
([True, None], [None, False], [True, False], "boolean"),
|
|
(
|
|
["a", None],
|
|
[None, "b"],
|
|
["a", "b"],
|
|
CategoricalDtype(categories=["a", "b"]),
|
|
),
|
|
(
|
|
[Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT],
|
|
[NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")],
|
|
[Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2,
|
|
"datetime64[ns, Europe/London]",
|
|
),
|
|
],
|
|
)
|
|
def test_update_extension_array_series(self, data, other, expected, dtype):
|
|
result = Series(data, dtype=dtype)
|
|
other = Series(other, dtype=dtype)
|
|
expected = Series(expected, dtype=dtype)
|
|
|
|
result.update(other)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_update_with_categorical_type(self):
|
|
# GH 25744
|
|
dtype = CategoricalDtype(["a", "b", "c", "d"])
|
|
s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype)
|
|
s2 = Series(["b", "a"], index=[1, 2], dtype=dtype)
|
|
s1.update(s2)
|
|
result = s1
|
|
expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype)
|
|
tm.assert_series_equal(result, expected)
|
|
|