134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas import (
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CategoricalDtype,
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DataFrame,
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NaT,
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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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class TestUpdate:
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def test_update(self, using_copy_on_write):
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s = Series([1.5, np.nan, 3.0, 4.0, np.nan])
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s2 = Series([np.nan, 3.5, np.nan, 5.0])
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s.update(s2)
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expected = Series([1.5, 3.5, 3.0, 5.0, np.nan])
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tm.assert_series_equal(s, expected)
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# GH 3217
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df = DataFrame([{"a": 1}, {"a": 3, "b": 2}])
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df["c"] = np.nan
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df_orig = df.copy()
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df["c"].update(Series(["foo"], index=[0]))
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if using_copy_on_write:
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expected = df_orig
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else:
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expected = DataFrame(
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[[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"]
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)
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize(
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"other, dtype, expected",
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[
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# other is int
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([61, 63], "int32", Series([10, 61, 12], dtype="int32")),
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([61, 63], "int64", Series([10, 61, 12])),
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([61, 63], float, Series([10.0, 61.0, 12.0])),
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([61, 63], object, Series([10, 61, 12], dtype=object)),
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# other is float, but can be cast to int
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([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32")),
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([61.0, 63.0], "int64", Series([10, 61, 12])),
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([61.0, 63.0], float, Series([10.0, 61.0, 12.0])),
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([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object)),
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# others is float, cannot be cast to int
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([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0])),
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([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0])),
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([61.1, 63.1], float, Series([10.0, 61.1, 12.0])),
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([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object)),
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# other is object, cannot be cast
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([(61,), (63,)], "int32", Series([10, (61,), 12])),
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([(61,), (63,)], "int64", Series([10, (61,), 12])),
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([(61,), (63,)], float, Series([10.0, (61,), 12.0])),
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([(61,), (63,)], object, Series([10, (61,), 12])),
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],
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)
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def test_update_dtypes(self, other, dtype, expected):
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ser = Series([10, 11, 12], dtype=dtype)
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other = Series(other, index=[1, 3])
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ser.update(other)
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tm.assert_series_equal(ser, expected)
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@pytest.mark.parametrize(
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"series, other, expected",
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[
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# update by key
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(
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Series({"a": 1, "b": 2, "c": 3, "d": 4}),
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{"b": 5, "c": np.nan},
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Series({"a": 1, "b": 5, "c": 3, "d": 4}),
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),
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# update by position
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(Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])),
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],
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)
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def test_update_from_non_series(self, series, other, expected):
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# GH 33215
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series.update(other)
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tm.assert_series_equal(series, expected)
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@pytest.mark.parametrize(
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"data, other, expected, dtype",
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[
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(["a", None], [None, "b"], ["a", "b"], "string[python]"),
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pytest.param(
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["a", None],
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[None, "b"],
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["a", "b"],
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"string[pyarrow]",
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marks=td.skip_if_no("pyarrow", min_version="1.0.0"),
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),
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([1, None], [None, 2], [1, 2], "Int64"),
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([True, None], [None, False], [True, False], "boolean"),
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(
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["a", None],
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[None, "b"],
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["a", "b"],
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CategoricalDtype(categories=["a", "b"]),
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),
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(
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[Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT],
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[NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")],
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[Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2,
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"datetime64[ns, Europe/London]",
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),
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],
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)
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def test_update_extension_array_series(self, data, other, expected, dtype):
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result = Series(data, dtype=dtype)
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other = Series(other, dtype=dtype)
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expected = Series(expected, dtype=dtype)
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result.update(other)
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tm.assert_series_equal(result, expected)
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def test_update_with_categorical_type(self):
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# GH 25744
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dtype = CategoricalDtype(["a", "b", "c", "d"])
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s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype)
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s2 = Series(["b", "a"], index=[1, 2], dtype=dtype)
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s1.update(s2)
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result = s1
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expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype)
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tm.assert_series_equal(result, expected)
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