def duplicated(self, keep="first"):
if isinstance(self, ABCIndexClass):
if self.is_unique:
return np.zeros(len(self), dtype=np.bool)
return duplicated(self, keep=keep)
else:
return self._constructor(duplicated(self, keep=keep),
index=self.index).__finalize__(self)
def duplicated(self, keep='first'):
from pandas.core.algorithms import duplicated
if isinstance(self, ABCIndexClass):
if self.is_unique:
return np.zeros(len(self), dtype=np.bool)
return duplicated(self, keep=keep)
else:
return self._constructor(duplicated(self, keep=keep),
index=self.index).__finalize__(self)
def duplicated(self, keep='first'):
from pandas.core.algorithms import duplicated
if isinstance(self, ABCIndexClass):
if self.is_unique:
return np.zeros(len(self), dtype=np.bool)
return duplicated(self, keep=keep)
else:
return self._constructor(duplicated(self, keep=keep),
index=self.index).__finalize__(self)
def test_datetime_likes(self):
dt = ['2011-01-01', '2011-01-02', '2011-01-01', 'NaT', '2011-01-03',
'2011-01-02', '2011-01-04', '2011-01-01', 'NaT', '2011-01-06']
td = ['1 days', '2 days', '1 days', 'NaT', '3 days',
'2 days', '4 days', '1 days', 'NaT', '6 days']
cases = [np.array([pd.Timestamp(d) for d in dt]),
np.array([pd.Timestamp(d, tz='US/Eastern') for d in dt]),
np.array([pd.Period(d, freq='D') for d in dt]),
np.array([np.datetime64(d) for d in dt]),
np.array([pd.Timedelta(d) for d in td])]
exp_first = np.array([False, False, True, False, False,
True, False, True, True, False])
exp_last = np.array([True, True, True, True, False,
False, False, False, False, False])
exp_false = exp_first | exp_last
for case in cases:
res_first = algos.duplicated(case, keep='first')
tm.assert_numpy_array_equal(res_first, exp_first)
res_last = algos.duplicated(case, keep='last')
tm.assert_numpy_array_equal(res_last, exp_last)
res_false = algos.duplicated(case, keep=False)
tm.assert_numpy_array_equal(res_false, exp_false)
# index
for idx in [pd.Index(case), pd.Index(case, dtype='category'),
pd.Index(case, dtype=object)]:
res_first = idx.duplicated(keep='first')
tm.assert_numpy_array_equal(res_first, exp_first)
res_last = idx.duplicated(keep='last')
tm.assert_numpy_array_equal(res_last, exp_last)
res_false = idx.duplicated(keep=False)
tm.assert_numpy_array_equal(res_false, exp_false)
# series
for s in [pd.Series(case), pd.Series(case, dtype='category'),
pd.Series(case, dtype=object)]:
res_first = s.duplicated(keep='first')
tm.assert_series_equal(res_first, pd.Series(exp_first))
res_last = s.duplicated(keep='last')
tm.assert_series_equal(res_last, pd.Series(exp_last))
res_false = s.duplicated(keep=False)
tm.assert_series_equal(res_false, pd.Series(exp_false))
def test_numeric_object_likes(self):
cases = [np.array([1, 2, 1, 5, 3,
2, 4, 1, 5, 6]),
np.array([1.1, 2.2, 1.1, np.nan, 3.3,
2.2, 4.4, 1.1, np.nan, 6.6]),
np.array([1 + 1j, 2 + 2j, 1 + 1j, 5 + 5j, 3 + 3j,
2 + 2j, 4 + 4j, 1 + 1j, 5 + 5j, 6 + 6j]),
np.array(['a', 'b', 'a', 'e', 'c',
'b', 'd', 'a', 'e', 'f'], dtype=object),
np.array([1, 2**63, 1, 3**5, 10,
2**63, 39, 1, 3**5, 7], dtype=np.uint64)]
exp_first = np.array([False, False, True, False, False,
True, False, True, True, False])
exp_last = np.array([True, True, True, True, False,
False, False, False, False, False])
exp_false = exp_first | exp_last
for case in cases:
res_first = algos.duplicated(case, keep='first')
tm.assert_numpy_array_equal(res_first, exp_first)
res_last = algos.duplicated(case, keep='last')
tm.assert_numpy_array_equal(res_last, exp_last)
res_false = algos.duplicated(case, keep=False)
tm.assert_numpy_array_equal(res_false, exp_false)
# index
for idx in [pd.Index(case), pd.Index(case, dtype='category')]:
res_first = idx.duplicated(keep='first')
tm.assert_numpy_array_equal(res_first, exp_first)
res_last = idx.duplicated(keep='last')
tm.assert_numpy_array_equal(res_last, exp_last)
res_false = idx.duplicated(keep=False)
tm.assert_numpy_array_equal(res_false, exp_false)
# series
for s in [pd.Series(case), pd.Series(case, dtype='category')]:
res_first = s.duplicated(keep='first')
tm.assert_series_equal(res_first, pd.Series(exp_first))
res_last = s.duplicated(keep='last')
tm.assert_series_equal(res_last, pd.Series(exp_last))
res_false = s.duplicated(keep=False)
tm.assert_series_equal(res_false, pd.Series(exp_false))
def test_duplicated_with_nas(self):
keys = np.array([0, 1, np.nan, 0, 2, np.nan], dtype=object)
result = algos.duplicated(keys)
expected = np.array([False, False, False, True, False, True])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='first')
expected = np.array([False, False, False, True, False, True])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='last')
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep=False)
expected = np.array([True, False, True, True, False, True])
tm.assert_numpy_array_equal(result, expected)
keys = np.empty(8, dtype=object)
for i, t in enumerate(zip([0, 0, np.nan, np.nan] * 2,
[0, np.nan, 0, np.nan] * 2)):
keys[i] = t
result = algos.duplicated(keys)
falses = [False] * 4
trues = [True] * 4
expected = np.array(falses + trues)
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='last')
expected = np.array(trues + falses)
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep=False)
expected = np.array(trues + trues)
tm.assert_numpy_array_equal(result, expected)
def test_duplicated_with_nas(self):
keys = np.array([0, 1, np.nan, 0, 2, np.nan], dtype=object)
result = algos.duplicated(keys)
expected = np.array([False, False, False, True, False, True])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='first')
expected = np.array([False, False, False, True, False, True])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='last')
expected = np.array([True, False, True, False, False, False])
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep=False)
expected = np.array([True, False, True, True, False, True])
tm.assert_numpy_array_equal(result, expected)
keys = np.empty(8, dtype=object)
for i, t in enumerate(zip([0, 0, np.nan, np.nan] * 2,
[0, np.nan, 0, np.nan] * 2)):
keys[i] = t
result = algos.duplicated(keys)
falses = [False] * 4
trues = [True] * 4
expected = np.array(falses + trues)
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep='last')
expected = np.array(trues + falses)
tm.assert_numpy_array_equal(result, expected)
result = algos.duplicated(keys, keep=False)
expected = np.array(trues + trues)
tm.assert_numpy_array_equal(result, expected)
def duplicated(self, keep="first"):
return duplicated(self._values, keep=keep)
def _duplicated(
self,
keep: Literal["first", "last", False] = "first") -> np.ndarray:
return duplicated(self._values, keep=keep)
def _duplicated(self, keep: Union[str, bool] = "first") -> np.ndarray:
return duplicated(self._values, keep=keep)
def _duplicated(
self,
keep: Literal["first", "last",
False] = "first") -> npt.NDArray[np.bool_]:
return duplicated(self._values, keep=keep)
def duplicated(self, keep: Union[str, bool] = "first") -> np.ndarray:
# error: Value of type variable "ArrayLike" of "duplicated" cannot be
# "Union[ExtensionArray, ndarray]"
return duplicated(self._values, keep=keep) # type: ignore[type-var]
def _duplicated(self, keep: str | bool = "first") -> np.ndarray:
return duplicated(self._values, keep=keep)