270 lines
8.4 KiB
Python
270 lines
8.4 KiB
Python
from distutils.version import LooseVersion
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from operator import methodcaller
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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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import pandas as pd
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from pandas import MultiIndex, Series, date_range
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import pandas.util.testing as tm
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from pandas.util.testing import assert_almost_equal, assert_series_equal
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from .test_generic import Generic
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try:
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import xarray
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_XARRAY_INSTALLED = True
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except ImportError:
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_XARRAY_INSTALLED = False
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class TestSeries(Generic):
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_typ = Series
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_comparator = lambda self, x, y: assert_series_equal(x, y)
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def setup_method(self):
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self.ts = tm.makeTimeSeries() # Was at top level in test_series
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self.ts.name = "ts"
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self.series = tm.makeStringSeries()
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self.series.name = "series"
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def test_rename_mi(self):
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s = Series(
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[11, 21, 31],
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index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]),
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)
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s.rename(str.lower)
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def test_set_axis_name(self):
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s = Series([1, 2, 3], index=["a", "b", "c"])
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funcs = ["rename_axis", "_set_axis_name"]
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name = "foo"
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for func in funcs:
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result = methodcaller(func, name)(s)
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assert s.index.name is None
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assert result.index.name == name
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def test_set_axis_name_mi(self):
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s = Series(
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[11, 21, 31],
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index=MultiIndex.from_tuples(
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[("A", x) for x in ["a", "B", "c"]], names=["l1", "l2"]
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),
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)
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funcs = ["rename_axis", "_set_axis_name"]
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for func in funcs:
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result = methodcaller(func, ["L1", "L2"])(s)
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assert s.index.name is None
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assert s.index.names == ["l1", "l2"]
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assert result.index.name is None
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assert result.index.names, ["L1", "L2"]
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def test_set_axis_name_raises(self):
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s = pd.Series([1])
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with pytest.raises(ValueError):
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s._set_axis_name(name="a", axis=1)
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def test_get_numeric_data_preserve_dtype(self):
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# get the numeric data
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o = Series([1, 2, 3])
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result = o._get_numeric_data()
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self._compare(result, o)
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o = Series([1, "2", 3.0])
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result = o._get_numeric_data()
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expected = Series([], dtype=object, index=pd.Index([], dtype=object))
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self._compare(result, expected)
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o = Series([True, False, True])
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result = o._get_numeric_data()
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self._compare(result, o)
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o = Series([True, False, True])
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result = o._get_bool_data()
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self._compare(result, o)
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o = Series(date_range("20130101", periods=3))
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result = o._get_numeric_data()
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expected = Series([], dtype="M8[ns]", index=pd.Index([], dtype=object))
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self._compare(result, expected)
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def test_nonzero_single_element(self):
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# allow single item via bool method
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s = Series([True])
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assert s.bool()
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s = Series([False])
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assert not s.bool()
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msg = "The truth value of a Series is ambiguous"
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# single item nan to raise
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for s in [Series([np.nan]), Series([pd.NaT]), Series([True]), Series([False])]:
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with pytest.raises(ValueError, match=msg):
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bool(s)
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msg = "bool cannot act on a non-boolean single element Series"
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for s in [Series([np.nan]), Series([pd.NaT])]:
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with pytest.raises(ValueError, match=msg):
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s.bool()
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# multiple bool are still an error
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msg = "The truth value of a Series is ambiguous"
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for s in [Series([True, True]), Series([False, False])]:
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with pytest.raises(ValueError, match=msg):
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bool(s)
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with pytest.raises(ValueError, match=msg):
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s.bool()
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# single non-bool are an error
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for s in [Series([1]), Series([0]), Series(["a"]), Series([0.0])]:
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msg = "The truth value of a Series is ambiguous"
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with pytest.raises(ValueError, match=msg):
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bool(s)
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msg = "bool cannot act on a non-boolean single element Series"
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with pytest.raises(ValueError, match=msg):
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s.bool()
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def test_metadata_propagation_indiv(self):
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# check that the metadata matches up on the resulting ops
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o = Series(range(3), range(3))
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o.name = "foo"
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o2 = Series(range(3), range(3))
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o2.name = "bar"
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result = o.T
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self.check_metadata(o, result)
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# resample
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ts = Series(
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np.random.rand(1000),
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index=date_range("20130101", periods=1000, freq="s"),
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name="foo",
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)
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result = ts.resample("1T").mean()
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self.check_metadata(ts, result)
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result = ts.resample("1T").min()
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self.check_metadata(ts, result)
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result = ts.resample("1T").apply(lambda x: x.sum())
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self.check_metadata(ts, result)
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_metadata = Series._metadata
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_finalize = Series.__finalize__
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Series._metadata = ["name", "filename"]
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o.filename = "foo"
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o2.filename = "bar"
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def finalize(self, other, method=None, **kwargs):
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for name in self._metadata:
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if method == "concat" and name == "filename":
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value = "+".join(
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[getattr(o, name) for o in other.objs if getattr(o, name, None)]
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)
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object.__setattr__(self, name, value)
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else:
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object.__setattr__(self, name, getattr(other, name, None))
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return self
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Series.__finalize__ = finalize
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result = pd.concat([o, o2])
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assert result.filename == "foo+bar"
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assert result.name is None
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# reset
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Series._metadata = _metadata
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Series.__finalize__ = _finalize
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@pytest.mark.skipif(
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not _XARRAY_INSTALLED
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or _XARRAY_INSTALLED
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and LooseVersion(xarray.__version__) < LooseVersion("0.10.0"),
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reason="xarray >= 0.10.0 required",
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)
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@pytest.mark.parametrize(
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"index",
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[
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"FloatIndex",
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"IntIndex",
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"StringIndex",
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"UnicodeIndex",
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"DateIndex",
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"PeriodIndex",
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"TimedeltaIndex",
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"CategoricalIndex",
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],
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)
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def test_to_xarray_index_types(self, index):
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from xarray import DataArray
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index = getattr(tm, "make{}".format(index))
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s = Series(range(6), index=index(6))
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s.index.name = "foo"
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result = s.to_xarray()
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repr(result)
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assert len(result) == 6
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assert len(result.coords) == 1
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assert_almost_equal(list(result.coords.keys()), ["foo"])
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assert isinstance(result, DataArray)
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# idempotency
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assert_series_equal(
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result.to_series(), s, check_index_type=False, check_categorical=True
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)
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@td.skip_if_no("xarray", min_version="0.7.0")
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def test_to_xarray(self):
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from xarray import DataArray
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s = Series([])
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s.index.name = "foo"
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result = s.to_xarray()
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assert len(result) == 0
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assert len(result.coords) == 1
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assert_almost_equal(list(result.coords.keys()), ["foo"])
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assert isinstance(result, DataArray)
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s = Series(range(6))
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s.index.name = "foo"
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s.index = pd.MultiIndex.from_product(
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[["a", "b"], range(3)], names=["one", "two"]
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)
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result = s.to_xarray()
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assert len(result) == 2
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assert_almost_equal(list(result.coords.keys()), ["one", "two"])
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assert isinstance(result, DataArray)
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assert_series_equal(result.to_series(), s)
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def test_valid_deprecated(self):
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# GH18800
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with tm.assert_produces_warning(FutureWarning):
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pd.Series([]).valid()
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@pytest.mark.parametrize(
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"s",
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[
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Series([np.arange(5)]),
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pd.date_range("1/1/2011", periods=24, freq="H"),
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pd.Series(range(5), index=pd.date_range("2017", periods=5)),
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],
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)
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@pytest.mark.parametrize("shift_size", [0, 1, 2])
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def test_shift_always_copy(self, s, shift_size):
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# GH22397
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assert s.shift(shift_size) is not s
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@pytest.mark.parametrize("move_by_freq", [pd.Timedelta("1D"), pd.Timedelta("1M")])
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def test_datetime_shift_always_copy(self, move_by_freq):
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# GH22397
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s = pd.Series(range(5), index=pd.date_range("2017", periods=5))
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assert s.shift(freq=move_by_freq) is not s
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