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# This file contains code vendored from pandas
# For reference, here is a copy of the pandas copyright notice:
# BSD 3-Clause License
# Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
# All rights reserved.
# Copyright (c) 2011-2025, Open source contributors.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import pandas as pd
import pandas._testing as tm
import pytest
from packaging.version import Version
from pandas import (
Categorical,
CategoricalIndex,
DataFrame,
Index,
IntervalIndex,
MultiIndex,
RangeIndex,
Series,
date_range,
period_range,
timedelta_range,
)
indices_dict: dict[str, Index] = {
"object": Index([f"pandas_{i}" for i in range(10)], dtype=object),
"string": Index([f"pandas_{i}" for i in range(10)], dtype="str"),
"datetime": date_range("2020-01-01", periods=10),
"datetime-tz": date_range("2020-01-01", periods=10, tz="US/Pacific"),
"period": period_range("2020-01-01", periods=10, freq="D"),
"timedelta": timedelta_range(start="1 day", periods=10, freq="D"),
"range": RangeIndex(10),
"int8": Index(np.arange(10), dtype="int8"),
"int16": Index(np.arange(10), dtype="int16"),
"int32": Index(np.arange(10), dtype="int32"),
"int64": Index(np.arange(10), dtype="int64"),
"uint8": Index(np.arange(10), dtype="uint8"),
"uint16": Index(np.arange(10), dtype="uint16"),
"uint32": Index(np.arange(10), dtype="uint32"),
"uint64": Index(np.arange(10), dtype="uint64"),
"float32": Index(np.arange(10), dtype="float32"),
"float64": Index(np.arange(10), dtype="float64"),
"bool-object": Index([True, False] * 5, dtype=object),
"bool-dtype": Index([True, False] * 5, dtype=bool),
"complex64": Index(
np.arange(10, dtype="complex64") + 1.0j * np.arange(10, dtype="complex64")
),
"complex128": Index(
np.arange(10, dtype="complex128") + 1.0j * np.arange(10, dtype="complex128")
),
"categorical": CategoricalIndex(list("abcd") * 2),
"interval": IntervalIndex.from_breaks(np.linspace(0, 100, num=11, dtype="int")),
"empty": Index([]),
# "tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),
# "mi-with-dt64tz-level": _create_mi_with_dt64tz_level(),
# "multi": _create_multiindex(),
"repeats": Index([0, 0, 1, 1, 2, 2]),
"nullable_int": Index(np.arange(10), dtype="Int64"),
"nullable_uint": Index(np.arange(10), dtype="UInt16"),
"nullable_float": Index(np.arange(10), dtype="Float32"),
"nullable_bool": Index(np.arange(10).astype(bool), dtype="boolean"),
"string-python": Index(
pd.array([f"pandas_{i}" for i in range(10)], dtype="string[python]")
),
}
@pytest.fixture(
params=[
key for key, value in indices_dict.items() if not isinstance(value, MultiIndex)
]
)
def index_flat(request):
"""
index fixture, but excluding MultiIndex cases.
"""
key = request.param
return indices_dict[key].copy()
class TestDataFrameToXArray:
@pytest.fixture
def df(self):
return DataFrame(
{
"a": list("abcd"),
"b": list(range(1, 5)),
"c": np.arange(3, 7).astype("u1"),
"d": np.arange(4.0, 8.0, dtype="float64"),
"e": [True, False, True, False],
"f": Categorical(list("abcd")),
"g": date_range("20130101", periods=4),
"h": date_range("20130101", periods=4, tz="US/Eastern"),
}
)
def test_to_xarray_index_types(self, index_flat, df):
index = index_flat
# MultiIndex is tested in test_to_xarray_with_multiindex
if len(index) == 0:
pytest.skip("Test doesn't make sense for empty index")
from xarray import Dataset
df.index = index[:4]
df.index.name = "foo"
df.columns.name = "bar"
result = df.to_xarray()
assert result.sizes["foo"] == 4
assert len(result.coords) == 1
assert len(result.data_vars) == 8
tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
assert isinstance(result, Dataset)
# idempotency
# datetimes w/tz are preserved
# column names are lost
expected = df.copy()
expected.columns.name = None
tm.assert_frame_equal(result.to_dataframe(), expected)
def test_to_xarray_empty(self, df):
from xarray import Dataset
df.index.name = "foo"
result = df[0:0].to_xarray()
assert result.sizes["foo"] == 0
assert isinstance(result, Dataset)
def test_to_xarray_with_multiindex(self, df):
from xarray import Dataset
# MultiIndex
df.index = MultiIndex.from_product([["a"], range(4)], names=["one", "two"])
result = df.to_xarray()
assert result.sizes["one"] == 1
assert result.sizes["two"] == 4
assert len(result.coords) == 2
assert len(result.data_vars) == 8
tm.assert_almost_equal(list(result.coords.keys()), ["one", "two"])
assert isinstance(result, Dataset)
result = result.to_dataframe()
expected = df.copy()
expected["f"] = expected["f"].astype(
object if Version(pd.__version__) < Version("3.0.0dev0") else str
)
expected.columns.name = None
tm.assert_frame_equal(result, expected)
class TestSeriesToXArray:
def test_to_xarray_index_types(self, index_flat):
index = index_flat
# MultiIndex is tested in test_to_xarray_with_multiindex
from xarray import DataArray
ser = Series(range(len(index)), index=index, dtype="int64")
ser.index.name = "foo"
result = ser.to_xarray()
repr(result)
assert len(result) == len(index)
assert len(result.coords) == 1
tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
assert isinstance(result, DataArray)
# idempotency
tm.assert_series_equal(result.to_series(), ser)
def test_to_xarray_empty(self):
from xarray import DataArray
ser = Series([], dtype=object)
ser.index.name = "foo"
result = ser.to_xarray()
assert len(result) == 0
assert len(result.coords) == 1
tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
assert isinstance(result, DataArray)
def test_to_xarray_with_multiindex(self):
from xarray import DataArray
mi = MultiIndex.from_product([["a", "b"], range(3)], names=["one", "two"])
ser = Series(range(6), dtype="int64", index=mi)
result = ser.to_xarray()
assert len(result) == 2
tm.assert_almost_equal(list(result.coords.keys()), ["one", "two"])
assert isinstance(result, DataArray)
res = result.to_series()
tm.assert_series_equal(res, ser)
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