760 lines
26 KiB
Python
760 lines
26 KiB
Python
from collections import OrderedDict
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import pydoc
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import warnings
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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DatetimeIndex,
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Index,
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Series,
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TimedeltaIndex,
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date_range,
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period_range,
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timedelta_range,
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)
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from pandas.core.arrays import PeriodArray
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from pandas.core.indexes.datetimes import Timestamp
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import pandas.util.testing as tm
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from pandas.util.testing import assert_series_equal, ensure_clean
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import pandas.io.formats.printing as printing
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from .common import TestData
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class SharedWithSparse:
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"""
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A collection of tests Series and SparseSeries can share.
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In generic tests on this class, use ``self._assert_series_equal()``
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which is implemented in sub-classes.
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"""
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def _assert_series_equal(self, left, right):
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"""Dispatch to series class dependent assertion"""
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raise NotImplementedError
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def test_scalarop_preserve_name(self):
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result = self.ts * 2
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assert result.name == self.ts.name
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def test_copy_name(self):
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result = self.ts.copy()
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assert result.name == self.ts.name
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def test_copy_index_name_checking(self):
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# don't want to be able to modify the index stored elsewhere after
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# making a copy
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self.ts.index.name = None
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assert self.ts.index.name is None
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assert self.ts is self.ts
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cp = self.ts.copy()
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cp.index.name = "foo"
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printing.pprint_thing(self.ts.index.name)
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assert self.ts.index.name is None
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def test_append_preserve_name(self):
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result = self.ts[:5].append(self.ts[5:])
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assert result.name == self.ts.name
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def test_binop_maybe_preserve_name(self):
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# names match, preserve
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result = self.ts * self.ts
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assert result.name == self.ts.name
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result = self.ts.mul(self.ts)
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assert result.name == self.ts.name
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result = self.ts * self.ts[:-2]
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assert result.name == self.ts.name
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# names don't match, don't preserve
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cp = self.ts.copy()
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cp.name = "something else"
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result = self.ts + cp
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assert result.name is None
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result = self.ts.add(cp)
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assert result.name is None
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ops = ["add", "sub", "mul", "div", "truediv", "floordiv", "mod", "pow"]
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ops = ops + ["r" + op for op in ops]
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for op in ops:
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# names match, preserve
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s = self.ts.copy()
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result = getattr(s, op)(s)
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assert result.name == self.ts.name
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# names don't match, don't preserve
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cp = self.ts.copy()
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cp.name = "changed"
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result = getattr(s, op)(cp)
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assert result.name is None
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def test_combine_first_name(self):
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result = self.ts.combine_first(self.ts[:5])
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assert result.name == self.ts.name
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def test_getitem_preserve_name(self):
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result = self.ts[self.ts > 0]
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assert result.name == self.ts.name
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result = self.ts[[0, 2, 4]]
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assert result.name == self.ts.name
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result = self.ts[5:10]
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assert result.name == self.ts.name
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def test_pickle(self):
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unp_series = self._pickle_roundtrip(self.series)
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unp_ts = self._pickle_roundtrip(self.ts)
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assert_series_equal(unp_series, self.series)
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assert_series_equal(unp_ts, self.ts)
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def _pickle_roundtrip(self, obj):
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with ensure_clean() as path:
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obj.to_pickle(path)
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unpickled = pd.read_pickle(path)
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return unpickled
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def test_argsort_preserve_name(self):
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result = self.ts.argsort()
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assert result.name == self.ts.name
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def test_sort_index_name(self):
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result = self.ts.sort_index(ascending=False)
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assert result.name == self.ts.name
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@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
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@pytest.mark.filterwarnings("ignore:Series.to_sparse:FutureWarning")
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def test_to_sparse_pass_name(self):
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result = self.ts.to_sparse()
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assert result.name == self.ts.name
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def test_constructor_dict(self):
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d = {"a": 0.0, "b": 1.0, "c": 2.0}
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result = self.series_klass(d)
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expected = self.series_klass(d, index=sorted(d.keys()))
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self._assert_series_equal(result, expected)
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result = self.series_klass(d, index=["b", "c", "d", "a"])
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expected = self.series_klass([1, 2, np.nan, 0], index=["b", "c", "d", "a"])
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self._assert_series_equal(result, expected)
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def test_constructor_subclass_dict(self):
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data = tm.TestSubDict((x, 10.0 * x) for x in range(10))
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series = self.series_klass(data)
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expected = self.series_klass(dict(data.items()))
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self._assert_series_equal(series, expected)
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def test_constructor_ordereddict(self):
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# GH3283
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data = OrderedDict(
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("col{i}".format(i=i), np.random.random()) for i in range(12)
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)
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series = self.series_klass(data)
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expected = self.series_klass(list(data.values()), list(data.keys()))
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self._assert_series_equal(series, expected)
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# Test with subclass
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class A(OrderedDict):
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pass
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series = self.series_klass(A(data))
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self._assert_series_equal(series, expected)
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def test_constructor_dict_multiindex(self):
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d = {("a", "a"): 0.0, ("b", "a"): 1.0, ("b", "c"): 2.0}
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_d = sorted(d.items())
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result = self.series_klass(d)
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expected = self.series_klass(
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[x[1] for x in _d], index=pd.MultiIndex.from_tuples([x[0] for x in _d])
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)
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self._assert_series_equal(result, expected)
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d["z"] = 111.0
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_d.insert(0, ("z", d["z"]))
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result = self.series_klass(d)
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expected = self.series_klass(
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[x[1] for x in _d], index=pd.Index([x[0] for x in _d], tupleize_cols=False)
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)
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result = result.reindex(index=expected.index)
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self._assert_series_equal(result, expected)
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def test_constructor_dict_timedelta_index(self):
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# GH #12169 : Resample category data with timedelta index
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# construct Series from dict as data and TimedeltaIndex as index
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# will result NaN in result Series data
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expected = self.series_klass(
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data=["A", "B", "C"], index=pd.to_timedelta([0, 10, 20], unit="s")
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)
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result = self.series_klass(
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data={
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pd.to_timedelta(0, unit="s"): "A",
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pd.to_timedelta(10, unit="s"): "B",
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pd.to_timedelta(20, unit="s"): "C",
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},
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index=pd.to_timedelta([0, 10, 20], unit="s"),
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)
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self._assert_series_equal(result, expected)
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@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
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def test_from_array_deprecated(self):
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# multiple FutureWarnings, so can't assert stacklevel
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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self.series_klass.from_array([1, 2, 3])
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def test_sparse_accessor_updates_on_inplace(self):
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s = pd.Series([1, 1, 2, 3], dtype="Sparse[int]")
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s.drop([0, 1], inplace=True)
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assert s.sparse.density == 1.0
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class TestSeriesMisc(TestData, SharedWithSparse):
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series_klass = Series
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# SharedWithSparse tests use generic, series_klass-agnostic assertion
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_assert_series_equal = staticmethod(tm.assert_series_equal)
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def test_tab_completion(self):
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# GH 9910
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s = Series(list("abcd"))
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# Series of str values should have .str but not .dt/.cat in __dir__
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assert "str" in dir(s)
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assert "dt" not in dir(s)
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assert "cat" not in dir(s)
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# similarly for .dt
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s = Series(date_range("1/1/2015", periods=5))
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assert "dt" in dir(s)
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assert "str" not in dir(s)
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assert "cat" not in dir(s)
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# Similarly for .cat, but with the twist that str and dt should be
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# there if the categories are of that type first cat and str.
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s = Series(list("abbcd"), dtype="category")
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assert "cat" in dir(s)
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assert "str" in dir(s) # as it is a string categorical
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assert "dt" not in dir(s)
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# similar to cat and str
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s = Series(date_range("1/1/2015", periods=5)).astype("category")
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assert "cat" in dir(s)
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assert "str" not in dir(s)
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assert "dt" in dir(s) # as it is a datetime categorical
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def test_tab_completion_with_categorical(self):
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# test the tab completion display
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ok_for_cat = [
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"name",
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"index",
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"categorical",
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"categories",
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"codes",
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"ordered",
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"set_categories",
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"add_categories",
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"remove_categories",
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"rename_categories",
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"reorder_categories",
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"remove_unused_categories",
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"as_ordered",
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"as_unordered",
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]
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def get_dir(s):
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results = [r for r in s.cat.__dir__() if not r.startswith("_")]
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return list(sorted(set(results)))
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s = Series(list("aabbcde")).astype("category")
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results = get_dir(s)
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tm.assert_almost_equal(results, list(sorted(set(ok_for_cat))))
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@pytest.mark.parametrize(
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"index",
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[
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tm.makeUnicodeIndex(10),
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tm.makeStringIndex(10),
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tm.makeCategoricalIndex(10),
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Index(["foo", "bar", "baz"] * 2),
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tm.makeDateIndex(10),
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tm.makePeriodIndex(10),
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tm.makeTimedeltaIndex(10),
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tm.makeIntIndex(10),
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tm.makeUIntIndex(10),
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tm.makeIntIndex(10),
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tm.makeFloatIndex(10),
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Index([True, False]),
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Index(["a{}".format(i) for i in range(101)]),
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pd.MultiIndex.from_tuples(zip("ABCD", "EFGH")),
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pd.MultiIndex.from_tuples(zip([0, 1, 2, 3], "EFGH")),
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],
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)
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def test_index_tab_completion(self, index):
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# dir contains string-like values of the Index.
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s = pd.Series(index=index)
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dir_s = dir(s)
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for i, x in enumerate(s.index.unique(level=0)):
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if i < 100:
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assert not isinstance(x, str) or not x.isidentifier() or x in dir_s
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else:
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assert x not in dir_s
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def test_not_hashable(self):
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s_empty = Series()
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s = Series([1])
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msg = "'Series' objects are mutable, thus they cannot be hashed"
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with pytest.raises(TypeError, match=msg):
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hash(s_empty)
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with pytest.raises(TypeError, match=msg):
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hash(s)
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def test_contains(self):
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tm.assert_contains_all(self.ts.index, self.ts)
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def test_iter(self):
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for i, val in enumerate(self.series):
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assert val == self.series[i]
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for i, val in enumerate(self.ts):
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assert val == self.ts[i]
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def test_keys(self):
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# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
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# to .keys() in a list()
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getkeys = self.ts.keys
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assert getkeys() is self.ts.index
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def test_values(self):
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tm.assert_almost_equal(self.ts.values, self.ts, check_dtype=False)
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def test_iteritems(self):
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for idx, val in self.series.iteritems():
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assert val == self.series[idx]
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for idx, val in self.ts.iteritems():
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assert val == self.ts[idx]
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# assert is lazy (genrators don't define reverse, lists do)
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assert not hasattr(self.series.iteritems(), "reverse")
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def test_items(self):
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for idx, val in self.series.items():
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assert val == self.series[idx]
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for idx, val in self.ts.items():
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assert val == self.ts[idx]
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# assert is lazy (genrators don't define reverse, lists do)
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assert not hasattr(self.series.items(), "reverse")
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def test_raise_on_info(self):
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s = Series(np.random.randn(10))
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msg = "'Series' object has no attribute 'info'"
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with pytest.raises(AttributeError, match=msg):
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s.info()
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def test_copy(self):
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for deep in [None, False, True]:
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s = Series(np.arange(10), dtype="float64")
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# default deep is True
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if deep is None:
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s2 = s.copy()
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else:
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s2 = s.copy(deep=deep)
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s2[::2] = np.NaN
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if deep is None or deep is True:
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# Did not modify original Series
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assert np.isnan(s2[0])
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assert not np.isnan(s[0])
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else:
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# we DID modify the original Series
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assert np.isnan(s2[0])
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assert np.isnan(s[0])
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def test_copy_tzaware(self):
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# GH#11794
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# copy of tz-aware
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expected = Series([Timestamp("2012/01/01", tz="UTC")])
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expected2 = Series([Timestamp("1999/01/01", tz="UTC")])
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for deep in [None, False, True]:
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s = Series([Timestamp("2012/01/01", tz="UTC")])
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if deep is None:
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s2 = s.copy()
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else:
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s2 = s.copy(deep=deep)
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s2[0] = pd.Timestamp("1999/01/01", tz="UTC")
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# default deep is True
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if deep is None or deep is True:
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# Did not modify original Series
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assert_series_equal(s2, expected2)
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assert_series_equal(s, expected)
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else:
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# we DID modify the original Series
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assert_series_equal(s2, expected2)
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assert_series_equal(s, expected2)
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def test_axis_alias(self):
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s = Series([1, 2, np.nan])
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assert_series_equal(s.dropna(axis="rows"), s.dropna(axis="index"))
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assert s.dropna().sum("rows") == 3
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assert s._get_axis_number("rows") == 0
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assert s._get_axis_name("rows") == "index"
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def test_class_axis(self):
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# https://github.com/pandas-dev/pandas/issues/18147
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# no exception and no empty docstring
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assert pydoc.getdoc(Series.index)
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def test_numpy_unique(self):
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# it works!
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np.unique(self.ts)
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def test_ndarray_compat(self):
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# test numpy compat with Series as sub-class of NDFrame
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tsdf = DataFrame(
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np.random.randn(1000, 3),
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columns=["A", "B", "C"],
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index=date_range("1/1/2000", periods=1000),
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)
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def f(x):
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return x[x.idxmax()]
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result = tsdf.apply(f)
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expected = tsdf.max()
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tm.assert_series_equal(result, expected)
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# .item()
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with tm.assert_produces_warning(FutureWarning):
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s = Series([1])
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result = s.item()
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assert result == 1
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assert s.item() == s.iloc[0]
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# using an ndarray like function
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s = Series(np.random.randn(10))
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result = Series(np.ones_like(s))
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expected = Series(1, index=range(10), dtype="float64")
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tm.assert_series_equal(result, expected)
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# ravel
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s = Series(np.random.randn(10))
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tm.assert_almost_equal(s.ravel(order="F"), s.values.ravel(order="F"))
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# compress
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# GH 6658
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s = Series([0, 1.0, -1], index=list("abc"))
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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result = np.compress(s > 0, s)
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tm.assert_series_equal(result, Series([1.0], index=["b"]))
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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result = np.compress(s < -1, s)
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# result empty Index(dtype=object) as the same as original
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exp = Series([], dtype="float64", index=Index([], dtype="object"))
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tm.assert_series_equal(result, exp)
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s = Series([0, 1.0, -1], index=[0.1, 0.2, 0.3])
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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result = np.compress(s > 0, s)
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tm.assert_series_equal(result, Series([1.0], index=[0.2]))
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with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
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result = np.compress(s < -1, s)
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# result empty Float64Index as the same as original
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exp = Series([], dtype="float64", index=Index([], dtype="float64"))
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tm.assert_series_equal(result, exp)
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def test_str_accessor_updates_on_inplace(self):
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s = pd.Series(list("abc"))
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s.drop([0], inplace=True)
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assert len(s.str.lower()) == 2
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def test_str_attribute(self):
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# GH9068
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methods = ["strip", "rstrip", "lstrip"]
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s = Series([" jack", "jill ", " jesse ", "frank"])
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for method in methods:
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expected = Series([getattr(str, method)(x) for x in s.values])
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|
assert_series_equal(getattr(Series.str, method)(s.str), expected)
|
|
|
|
# str accessor only valid with string values
|
|
s = Series(range(5))
|
|
with pytest.raises(AttributeError, match="only use .str accessor"):
|
|
s.str.repeat(2)
|
|
|
|
def test_empty_method(self):
|
|
s_empty = pd.Series()
|
|
assert s_empty.empty
|
|
|
|
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
|
|
assert not full_series.empty
|
|
|
|
def test_tab_complete_warning(self, ip):
|
|
# https://github.com/pandas-dev/pandas/issues/16409
|
|
pytest.importorskip("IPython", minversion="6.0.0")
|
|
from IPython.core.completer import provisionalcompleter
|
|
|
|
code = "import pandas as pd; s = pd.Series()"
|
|
ip.run_code(code)
|
|
with tm.assert_produces_warning(None):
|
|
with provisionalcompleter("ignore"):
|
|
list(ip.Completer.completions("s.", 1))
|
|
|
|
def test_integer_series_size(self):
|
|
# GH 25580
|
|
s = Series(range(9))
|
|
assert s.size == 9
|
|
s = Series(range(9), dtype="Int64")
|
|
assert s.size == 9
|
|
|
|
def test_get_values_deprecation(self):
|
|
s = Series(range(9))
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
res = s.get_values()
|
|
tm.assert_numpy_array_equal(res, s.values)
|
|
|
|
|
|
class TestCategoricalSeries:
|
|
@pytest.mark.parametrize(
|
|
"method",
|
|
[
|
|
lambda x: x.cat.set_categories([1, 2, 3]),
|
|
lambda x: x.cat.reorder_categories([2, 3, 1], ordered=True),
|
|
lambda x: x.cat.rename_categories([1, 2, 3]),
|
|
lambda x: x.cat.remove_unused_categories(),
|
|
lambda x: x.cat.remove_categories([2]),
|
|
lambda x: x.cat.add_categories([4]),
|
|
lambda x: x.cat.as_ordered(),
|
|
lambda x: x.cat.as_unordered(),
|
|
],
|
|
)
|
|
def test_getname_categorical_accessor(self, method):
|
|
# GH 17509
|
|
s = Series([1, 2, 3], name="A").astype("category")
|
|
expected = "A"
|
|
result = method(s).name
|
|
assert result == expected
|
|
|
|
def test_cat_accessor(self):
|
|
s = Series(Categorical(["a", "b", np.nan, "a"]))
|
|
tm.assert_index_equal(s.cat.categories, Index(["a", "b"]))
|
|
assert not s.cat.ordered, False
|
|
|
|
exp = Categorical(["a", "b", np.nan, "a"], categories=["b", "a"])
|
|
s.cat.set_categories(["b", "a"], inplace=True)
|
|
tm.assert_categorical_equal(s.values, exp)
|
|
|
|
res = s.cat.set_categories(["b", "a"])
|
|
tm.assert_categorical_equal(res.values, exp)
|
|
|
|
s[:] = "a"
|
|
s = s.cat.remove_unused_categories()
|
|
tm.assert_index_equal(s.cat.categories, Index(["a"]))
|
|
|
|
def test_cat_accessor_api(self):
|
|
# GH 9322
|
|
from pandas.core.arrays.categorical import CategoricalAccessor
|
|
|
|
assert Series.cat is CategoricalAccessor
|
|
s = Series(list("aabbcde")).astype("category")
|
|
assert isinstance(s.cat, CategoricalAccessor)
|
|
|
|
invalid = Series([1])
|
|
with pytest.raises(AttributeError, match="only use .cat accessor"):
|
|
invalid.cat
|
|
assert not hasattr(invalid, "cat")
|
|
|
|
def test_cat_accessor_no_new_attributes(self):
|
|
# https://github.com/pandas-dev/pandas/issues/10673
|
|
c = Series(list("aabbcde")).astype("category")
|
|
with pytest.raises(AttributeError, match="You cannot add any new attribute"):
|
|
c.cat.xlabel = "a"
|
|
|
|
def test_cat_accessor_updates_on_inplace(self):
|
|
s = Series(list("abc")).astype("category")
|
|
s.drop(0, inplace=True)
|
|
s.cat.remove_unused_categories(inplace=True)
|
|
assert len(s.cat.categories) == 2
|
|
|
|
def test_categorical_delegations(self):
|
|
|
|
# invalid accessor
|
|
msg = r"Can only use \.cat accessor with a 'category' dtype"
|
|
with pytest.raises(AttributeError, match=msg):
|
|
Series([1, 2, 3]).cat
|
|
with pytest.raises(AttributeError, match=msg):
|
|
Series([1, 2, 3]).cat()
|
|
with pytest.raises(AttributeError, match=msg):
|
|
Series(["a", "b", "c"]).cat
|
|
with pytest.raises(AttributeError, match=msg):
|
|
Series(np.arange(5.0)).cat
|
|
with pytest.raises(AttributeError, match=msg):
|
|
Series([Timestamp("20130101")]).cat
|
|
|
|
# Series should delegate calls to '.categories', '.codes', '.ordered'
|
|
# and the methods '.set_categories()' 'drop_unused_categories()' to the
|
|
# categorical
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
|
exp_categories = Index(["a", "b", "c"])
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
|
s.cat.categories = [1, 2, 3]
|
|
exp_categories = Index([1, 2, 3])
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
|
|
|
exp_codes = Series([0, 1, 2, 0], dtype="int8")
|
|
tm.assert_series_equal(s.cat.codes, exp_codes)
|
|
|
|
assert s.cat.ordered
|
|
s = s.cat.as_unordered()
|
|
assert not s.cat.ordered
|
|
s.cat.as_ordered(inplace=True)
|
|
assert s.cat.ordered
|
|
|
|
# reorder
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
|
exp_categories = Index(["c", "b", "a"])
|
|
exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
|
|
s = s.cat.set_categories(["c", "b", "a"])
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
|
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
|
|
tm.assert_numpy_array_equal(s.__array__(), exp_values)
|
|
|
|
# remove unused categories
|
|
s = Series(Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"]))
|
|
exp_categories = Index(["a", "b"])
|
|
exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_)
|
|
s = s.cat.remove_unused_categories()
|
|
tm.assert_index_equal(s.cat.categories, exp_categories)
|
|
tm.assert_numpy_array_equal(s.values.__array__(), exp_values)
|
|
tm.assert_numpy_array_equal(s.__array__(), exp_values)
|
|
|
|
# This method is likely to be confused, so test that it raises an error
|
|
# on wrong inputs:
|
|
msg = "'Series' object has no attribute 'set_categories'"
|
|
with pytest.raises(AttributeError, match=msg):
|
|
s.set_categories([4, 3, 2, 1])
|
|
|
|
# right: s.cat.set_categories([4,3,2,1])
|
|
|
|
# GH18862 (let Series.cat.rename_categories take callables)
|
|
s = Series(Categorical(["a", "b", "c", "a"], ordered=True))
|
|
result = s.cat.rename_categories(lambda x: x.upper())
|
|
expected = Series(
|
|
Categorical(["A", "B", "C", "A"], categories=["A", "B", "C"], ordered=True)
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_dt_accessor_api_for_categorical(self):
|
|
# https://github.com/pandas-dev/pandas/issues/10661
|
|
from pandas.core.indexes.accessors import Properties
|
|
|
|
s_dr = Series(date_range("1/1/2015", periods=5, tz="MET"))
|
|
c_dr = s_dr.astype("category")
|
|
|
|
s_pr = Series(period_range("1/1/2015", freq="D", periods=5))
|
|
c_pr = s_pr.astype("category")
|
|
|
|
s_tdr = Series(timedelta_range("1 days", "10 days"))
|
|
c_tdr = s_tdr.astype("category")
|
|
|
|
# only testing field (like .day)
|
|
# and bool (is_month_start)
|
|
get_ops = lambda x: x._datetimelike_ops
|
|
|
|
test_data = [
|
|
("Datetime", get_ops(DatetimeIndex), s_dr, c_dr),
|
|
("Period", get_ops(PeriodArray), s_pr, c_pr),
|
|
("Timedelta", get_ops(TimedeltaIndex), s_tdr, c_tdr),
|
|
]
|
|
|
|
assert isinstance(c_dr.dt, Properties)
|
|
|
|
special_func_defs = [
|
|
("strftime", ("%Y-%m-%d",), {}),
|
|
("tz_convert", ("EST",), {}),
|
|
("round", ("D",), {}),
|
|
("floor", ("D",), {}),
|
|
("ceil", ("D",), {}),
|
|
("asfreq", ("D",), {}),
|
|
# ('tz_localize', ("UTC",), {}),
|
|
]
|
|
_special_func_names = [f[0] for f in special_func_defs]
|
|
|
|
# the series is already localized
|
|
_ignore_names = ["tz_localize", "components"]
|
|
|
|
for name, attr_names, s, c in test_data:
|
|
func_names = [
|
|
f
|
|
for f in dir(s.dt)
|
|
if not (
|
|
f.startswith("_")
|
|
or f in attr_names
|
|
or f in _special_func_names
|
|
or f in _ignore_names
|
|
)
|
|
]
|
|
|
|
func_defs = [(f, (), {}) for f in func_names]
|
|
for f_def in special_func_defs:
|
|
if f_def[0] in dir(s.dt):
|
|
func_defs.append(f_def)
|
|
|
|
for func, args, kwargs in func_defs:
|
|
with warnings.catch_warnings():
|
|
if func == "to_period":
|
|
# dropping TZ
|
|
warnings.simplefilter("ignore", UserWarning)
|
|
res = getattr(c.dt, func)(*args, **kwargs)
|
|
exp = getattr(s.dt, func)(*args, **kwargs)
|
|
|
|
if isinstance(res, DataFrame):
|
|
tm.assert_frame_equal(res, exp)
|
|
elif isinstance(res, Series):
|
|
tm.assert_series_equal(res, exp)
|
|
else:
|
|
tm.assert_almost_equal(res, exp)
|
|
|
|
for attr in attr_names:
|
|
try:
|
|
res = getattr(c.dt, attr)
|
|
exp = getattr(s.dt, attr)
|
|
except Exception as e:
|
|
print(name, attr)
|
|
raise e
|
|
|
|
if isinstance(res, DataFrame):
|
|
tm.assert_frame_equal(res, exp)
|
|
elif isinstance(res, Series):
|
|
tm.assert_series_equal(res, exp)
|
|
else:
|
|
tm.assert_almost_equal(res, exp)
|
|
|
|
invalid = Series([1, 2, 3]).astype("category")
|
|
msg = "Can only use .dt accessor with datetimelike"
|
|
|
|
with pytest.raises(AttributeError, match=msg):
|
|
invalid.dt
|
|
assert not hasattr(invalid, "str")
|