82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
import numpy as np
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from pandas import (
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MultiIndex,
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Series,
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date_range,
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)
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import pandas._testing as tm
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def test_xs_datetimelike_wrapping():
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# GH#31630 a case where we shouldn't wrap datetime64 in Timestamp
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arr = date_range("2016-01-01", periods=3)._data._data
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ser = Series(arr, dtype=object)
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for i in range(len(ser)):
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ser.iloc[i] = arr[i]
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assert ser.dtype == object
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assert isinstance(ser[0], np.datetime64)
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result = ser.xs(0)
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assert isinstance(result, np.datetime64)
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class TestXSWithMultiIndex:
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def test_xs_level_series(self, multiindex_dataframe_random_data):
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df = multiindex_dataframe_random_data
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ser = df["A"]
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expected = ser[:, "two"]
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result = df.xs("two", level=1)["A"]
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tm.assert_series_equal(result, expected)
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def test_series_getitem_multiindex_xs_by_label(self):
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# GH#5684
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idx = MultiIndex.from_tuples(
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[("a", "one"), ("a", "two"), ("b", "one"), ("b", "two")]
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)
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ser = Series([1, 2, 3, 4], index=idx)
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return_value = ser.index.set_names(["L1", "L2"], inplace=True)
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assert return_value is None
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expected = Series([1, 3], index=["a", "b"])
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return_value = expected.index.set_names(["L1"], inplace=True)
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assert return_value is None
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result = ser.xs("one", level="L2")
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tm.assert_series_equal(result, expected)
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def test_series_getitem_multiindex_xs(xs):
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# GH#6258
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dt = list(date_range("20130903", periods=3))
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idx = MultiIndex.from_product([list("AB"), dt])
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ser = Series([1, 3, 4, 1, 3, 4], index=idx)
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expected = Series([1, 1], index=list("AB"))
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result = ser.xs("20130903", level=1)
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tm.assert_series_equal(result, expected)
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def test_series_xs_droplevel_false(self):
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# GH: 19056
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mi = MultiIndex.from_tuples(
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[("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"]
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)
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ser = Series([1, 1, 1], index=mi)
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result = ser.xs("a", axis=0, drop_level=False)
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expected = Series(
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[1, 1],
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index=MultiIndex.from_tuples(
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[("a", "x"), ("a", "y")], names=["level1", "level2"]
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),
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)
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tm.assert_series_equal(result, expected)
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def test_xs_key_as_list(self):
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# GH#41760
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mi = MultiIndex.from_tuples([("a", "x")], names=["level1", "level2"])
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ser = Series([1], index=mi)
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with tm.assert_produces_warning(FutureWarning):
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ser.xs(["a", "x"], axis=0, drop_level=False)
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with tm.assert_produces_warning(FutureWarning):
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ser.xs(["a"], axis=0, drop_level=False)
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