from datetime import datetime, timedelta from io import StringIO import sys import numpy as np import pytest from pandas._libs.tslib import iNaT from pandas.compat import PYPY from pandas.compat.numpy import np_array_datetime64_compat from pandas.core.dtypes.common import ( is_datetime64_dtype, is_datetime64tz_dtype, is_object_dtype, needs_i8_conversion, ) import pandas as pd from pandas import ( DataFrame, DatetimeIndex, Index, Interval, IntervalIndex, PeriodIndex, Series, Timedelta, TimedeltaIndex, Timestamp, ) import pandas._testing as tm from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin class Ops: def _allow_na_ops(self, obj): """Whether to skip test cases including NaN""" if (isinstance(obj, Index) and obj.is_boolean()) or not obj._can_hold_na: # don't test boolean / integer dtypes return False return True def setup_method(self, method): self.bool_index = tm.makeBoolIndex(10, name="a") self.int_index = tm.makeIntIndex(10, name="a") self.float_index = tm.makeFloatIndex(10, name="a") self.dt_index = tm.makeDateIndex(10, name="a") self.dt_tz_index = tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern") self.period_index = tm.makePeriodIndex(10, name="a") self.string_index = tm.makeStringIndex(10, name="a") self.unicode_index = tm.makeUnicodeIndex(10, name="a") arr = np.random.randn(10) self.bool_series = Series(arr, index=self.bool_index, name="a") self.int_series = Series(arr, index=self.int_index, name="a") self.float_series = Series(arr, index=self.float_index, name="a") self.dt_series = Series(arr, index=self.dt_index, name="a") self.dt_tz_series = self.dt_tz_index.to_series() self.period_series = Series(arr, index=self.period_index, name="a") self.string_series = Series(arr, index=self.string_index, name="a") self.unicode_series = Series(arr, index=self.unicode_index, name="a") types = ["bool", "int", "float", "dt", "dt_tz", "period", "string", "unicode"] self.indexes = [getattr(self, f"{t}_index") for t in types] self.series = [getattr(self, f"{t}_series") for t in types] # To test narrow dtypes, we use narrower *data* elements, not *index* elements index = self.int_index self.float32_series = Series(arr.astype(np.float32), index=index, name="a") arr_int = np.random.choice(10, size=10, replace=False) self.int8_series = Series(arr_int.astype(np.int8), index=index, name="a") self.int16_series = Series(arr_int.astype(np.int16), index=index, name="a") self.int32_series = Series(arr_int.astype(np.int32), index=index, name="a") self.uint8_series = Series(arr_int.astype(np.uint8), index=index, name="a") self.uint16_series = Series(arr_int.astype(np.uint16), index=index, name="a") self.uint32_series = Series(arr_int.astype(np.uint32), index=index, name="a") nrw_types = ["float32", "int8", "int16", "int32", "uint8", "uint16", "uint32"] self.narrow_series = [getattr(self, f"{t}_series") for t in nrw_types] self.objs = self.indexes + self.series + self.narrow_series def check_ops_properties(self, props, filter=None, ignore_failures=False): for op in props: for o in self.is_valid_objs: # if a filter, skip if it doesn't match if filter is not None: filt = o.index if isinstance(o, Series) else o if not filter(filt): continue try: if isinstance(o, Series): expected = Series(getattr(o.index, op), index=o.index, name="a") else: expected = getattr(o, op) except (AttributeError): if ignore_failures: continue result = getattr(o, op) # these could be series, arrays or scalars if isinstance(result, Series) and isinstance(expected, Series): tm.assert_series_equal(result, expected) elif isinstance(result, Index) and isinstance(expected, Index): tm.assert_index_equal(result, expected) elif isinstance(result, np.ndarray) and isinstance( expected, np.ndarray ): tm.assert_numpy_array_equal(result, expected) else: assert result == expected # freq raises AttributeError on an Int64Index because its not # defined we mostly care about Series here anyhow if not ignore_failures: for o in self.not_valid_objs: # an object that is datetimelike will raise a TypeError, # otherwise an AttributeError err = AttributeError if issubclass(type(o), DatetimeIndexOpsMixin): err = TypeError with pytest.raises(err): getattr(o, op) @pytest.mark.parametrize("klass", [Series, DataFrame]) def test_binary_ops_docs(self, klass): op_map = { "add": "+", "sub": "-", "mul": "*", "mod": "%", "pow": "**", "truediv": "/", "floordiv": "//", } for op_name in op_map: operand1 = klass.__name__.lower() operand2 = "other" op = op_map[op_name] expected_str = " ".join([operand1, op, operand2]) assert expected_str in getattr(klass, op_name).__doc__ # reverse version of the binary ops expected_str = " ".join([operand2, op, operand1]) assert expected_str in getattr(klass, "r" + op_name).__doc__ class TestTranspose(Ops): errmsg = "the 'axes' parameter is not supported" def test_transpose(self): for obj in self.objs: tm.assert_equal(obj.transpose(), obj) def test_transpose_non_default_axes(self): for obj in self.objs: with pytest.raises(ValueError, match=self.errmsg): obj.transpose(1) with pytest.raises(ValueError, match=self.errmsg): obj.transpose(axes=1) def test_numpy_transpose(self): for obj in self.objs: tm.assert_equal(np.transpose(obj), obj) with pytest.raises(ValueError, match=self.errmsg): np.transpose(obj, axes=1) class TestIndexOps(Ops): def setup_method(self, method): super().setup_method(method) self.is_valid_objs = self.objs self.not_valid_objs = [] def test_none_comparison(self): # bug brought up by #1079 # changed from TypeError in 0.17.0 for o in self.is_valid_objs: if isinstance(o, Series): o[0] = np.nan # noinspection PyComparisonWithNone result = o == None # noqa assert not result.iat[0] assert not result.iat[1] # noinspection PyComparisonWithNone result = o != None # noqa assert result.iat[0] assert result.iat[1] result = None == o # noqa assert not result.iat[0] assert not result.iat[1] result = None != o # noqa assert result.iat[0] assert result.iat[1] if is_datetime64_dtype(o) or is_datetime64tz_dtype(o): # Following DatetimeIndex (and Timestamp) convention, # inequality comparisons with Series[datetime64] raise with pytest.raises(TypeError): None > o with pytest.raises(TypeError): o > None else: result = None > o assert not result.iat[0] assert not result.iat[1] result = o < None assert not result.iat[0] assert not result.iat[1] def test_ndarray_compat_properties(self): for o in self.objs: # Check that we work. for p in ["shape", "dtype", "T", "nbytes"]: assert getattr(o, p, None) is not None # deprecated properties for p in ["flags", "strides", "itemsize", "base", "data"]: assert not hasattr(o, p) with pytest.raises(ValueError): o.item() # len > 1 assert o.ndim == 1 assert o.size == len(o) assert Index([1]).item() == 1 assert Series([1]).item() == 1 def test_value_counts_unique_nunique(self): for orig in self.objs: o = orig.copy() klass = type(o) values = o._values if isinstance(values, Index): # reset name not to affect latter process values.name = None # create repeated values, 'n'th element is repeated by n+1 times # skip boolean, because it only has 2 values at most if isinstance(o, Index) and o.is_boolean(): continue elif isinstance(o, Index): expected_index = Index(o[::-1]) expected_index.name = None o = o.repeat(range(1, len(o) + 1)) o.name = "a" else: expected_index = Index(values[::-1]) idx = o.index.repeat(range(1, len(o) + 1)) # take-based repeat indices = np.repeat(np.arange(len(o)), range(1, len(o) + 1)) rep = values.take(indices) o = klass(rep, index=idx, name="a") # check values has the same dtype as the original assert o.dtype == orig.dtype expected_s = Series( range(10, 0, -1), index=expected_index, dtype="int64", name="a" ) result = o.value_counts() tm.assert_series_equal(result, expected_s) assert result.index.name is None assert result.name == "a" result = o.unique() if isinstance(o, Index): assert isinstance(result, type(o)) tm.assert_index_equal(result, orig) assert result.dtype == orig.dtype elif is_datetime64tz_dtype(o): # datetimetz Series returns array of Timestamp assert result[0] == orig[0] for r in result: assert isinstance(r, Timestamp) tm.assert_numpy_array_equal( result.astype(object), orig._values.astype(object) ) else: tm.assert_numpy_array_equal(result, orig.values) assert result.dtype == orig.dtype assert o.nunique() == len(np.unique(o.values)) @pytest.mark.parametrize("null_obj", [np.nan, None]) def test_value_counts_unique_nunique_null(self, null_obj): for orig in self.objs: o = orig.copy() klass = type(o) values = o._ndarray_values if not self._allow_na_ops(o): continue # special assign to the numpy array if is_datetime64tz_dtype(o): if isinstance(o, DatetimeIndex): v = o.asi8 v[0:2] = iNaT values = o._shallow_copy(v) else: o = o.copy() o[0:2] = pd.NaT values = o._values elif needs_i8_conversion(o): values[0:2] = iNaT values = o._shallow_copy(values) else: values[0:2] = null_obj # check values has the same dtype as the original assert values.dtype == o.dtype # create repeated values, 'n'th element is repeated by n+1 # times if isinstance(o, (DatetimeIndex, PeriodIndex)): expected_index = o.copy() expected_index.name = None # attach name to klass o = klass(values.repeat(range(1, len(o) + 1))) o.name = "a" else: if isinstance(o, DatetimeIndex): expected_index = orig._values._shallow_copy(values) else: expected_index = Index(values) expected_index.name = None o = o.repeat(range(1, len(o) + 1)) o.name = "a" # check values has the same dtype as the original assert o.dtype == orig.dtype # check values correctly have NaN nanloc = np.zeros(len(o), dtype=np.bool) nanloc[:3] = True if isinstance(o, Index): tm.assert_numpy_array_equal(pd.isna(o), nanloc) else: exp = Series(nanloc, o.index, name="a") tm.assert_series_equal(pd.isna(o), exp) expected_s_na = Series( list(range(10, 2, -1)) + [3], index=expected_index[9:0:-1], dtype="int64", name="a", ) expected_s = Series( list(range(10, 2, -1)), index=expected_index[9:1:-1], dtype="int64", name="a", ) result_s_na = o.value_counts(dropna=False) tm.assert_series_equal(result_s_na, expected_s_na) assert result_s_na.index.name is None assert result_s_na.name == "a" result_s = o.value_counts() tm.assert_series_equal(o.value_counts(), expected_s) assert result_s.index.name is None assert result_s.name == "a" result = o.unique() if isinstance(o, Index): tm.assert_index_equal(result, Index(values[1:], name="a")) elif is_datetime64tz_dtype(o): # unable to compare NaT / nan tm.assert_extension_array_equal(result[1:], values[2:]) assert result[0] is pd.NaT else: tm.assert_numpy_array_equal(result[1:], values[2:]) assert pd.isna(result[0]) assert result.dtype == orig.dtype assert o.nunique() == 8 assert o.nunique(dropna=False) == 9 def test_value_counts_inferred(self, index_or_series): klass = index_or_series s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"] s = klass(s_values) expected = Series([4, 3, 2, 1], index=["b", "a", "d", "c"]) tm.assert_series_equal(s.value_counts(), expected) if isinstance(s, Index): exp = Index(np.unique(np.array(s_values, dtype=np.object_))) tm.assert_index_equal(s.unique(), exp) else: exp = np.unique(np.array(s_values, dtype=np.object_)) tm.assert_numpy_array_equal(s.unique(), exp) assert s.nunique() == 4 # don't sort, have to sort after the fact as not sorting is # platform-dep hist = s.value_counts(sort=False).sort_values() expected = Series([3, 1, 4, 2], index=list("acbd")).sort_values() tm.assert_series_equal(hist, expected) # sort ascending hist = s.value_counts(ascending=True) expected = Series([1, 2, 3, 4], index=list("cdab")) tm.assert_series_equal(hist, expected) # relative histogram. hist = s.value_counts(normalize=True) expected = Series([0.4, 0.3, 0.2, 0.1], index=["b", "a", "d", "c"]) tm.assert_series_equal(hist, expected) def test_value_counts_bins(self, index_or_series): klass = index_or_series s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"] s = klass(s_values) # bins with pytest.raises(TypeError): s.value_counts(bins=1) s1 = Series([1, 1, 2, 3]) res1 = s1.value_counts(bins=1) exp1 = Series({Interval(0.997, 3.0): 4}) tm.assert_series_equal(res1, exp1) res1n = s1.value_counts(bins=1, normalize=True) exp1n = Series({Interval(0.997, 3.0): 1.0}) tm.assert_series_equal(res1n, exp1n) if isinstance(s1, Index): tm.assert_index_equal(s1.unique(), Index([1, 2, 3])) else: exp = np.array([1, 2, 3], dtype=np.int64) tm.assert_numpy_array_equal(s1.unique(), exp) assert s1.nunique() == 3 # these return the same res4 = s1.value_counts(bins=4, dropna=True) intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0]) exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4, exp4) res4 = s1.value_counts(bins=4, dropna=False) intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0]) exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4, exp4) res4n = s1.value_counts(bins=4, normalize=True) exp4n = Series([0.5, 0.25, 0.25, 0], index=intervals.take([0, 3, 1, 2])) tm.assert_series_equal(res4n, exp4n) # handle NA's properly s_values = ["a", "b", "b", "b", np.nan, np.nan, "d", "d", "a", "a", "b"] s = klass(s_values) expected = Series([4, 3, 2], index=["b", "a", "d"]) tm.assert_series_equal(s.value_counts(), expected) if isinstance(s, Index): exp = Index(["a", "b", np.nan, "d"]) tm.assert_index_equal(s.unique(), exp) else: exp = np.array(["a", "b", np.nan, "d"], dtype=object) tm.assert_numpy_array_equal(s.unique(), exp) assert s.nunique() == 3 s = klass({}) if klass is dict else klass({}, dtype=object) expected = Series([], dtype=np.int64) tm.assert_series_equal(s.value_counts(), expected, check_index_type=False) # returned dtype differs depending on original if isinstance(s, Index): tm.assert_index_equal(s.unique(), Index([]), exact=False) else: tm.assert_numpy_array_equal(s.unique(), np.array([]), check_dtype=False) assert s.nunique() == 0 def test_value_counts_datetime64(self, index_or_series): klass = index_or_series # GH 3002, datetime64[ns] # don't test names though txt = "\n".join( [ "xxyyzz20100101PIE", "xxyyzz20100101GUM", "xxyyzz20100101EGG", "xxyyww20090101EGG", "foofoo20080909PIE", "foofoo20080909GUM", ] ) f = StringIO(txt) df = pd.read_fwf( f, widths=[6, 8, 3], names=["person_id", "dt", "food"], parse_dates=["dt"] ) s = klass(df["dt"].copy()) s.name = None idx = pd.to_datetime( ["2010-01-01 00:00:00", "2008-09-09 00:00:00", "2009-01-01 00:00:00"] ) expected_s = Series([3, 2, 1], index=idx) tm.assert_series_equal(s.value_counts(), expected_s) expected = np_array_datetime64_compat( ["2010-01-01 00:00:00", "2009-01-01 00:00:00", "2008-09-09 00:00:00"], dtype="datetime64[ns]", ) if isinstance(s, Index): tm.assert_index_equal(s.unique(), DatetimeIndex(expected)) else: tm.assert_numpy_array_equal(s.unique(), expected) assert s.nunique() == 3 # with NaT s = df["dt"].copy() s = klass(list(s.values) + [pd.NaT]) result = s.value_counts() assert result.index.dtype == "datetime64[ns]" tm.assert_series_equal(result, expected_s) result = s.value_counts(dropna=False) expected_s[pd.NaT] = 1 tm.assert_series_equal(result, expected_s) unique = s.unique() assert unique.dtype == "datetime64[ns]" # numpy_array_equal cannot compare pd.NaT if isinstance(s, Index): exp_idx = DatetimeIndex(expected.tolist() + [pd.NaT]) tm.assert_index_equal(unique, exp_idx) else: tm.assert_numpy_array_equal(unique[:3], expected) assert pd.isna(unique[3]) assert s.nunique() == 3 assert s.nunique(dropna=False) == 4 # timedelta64[ns] td = df.dt - df.dt + timedelta(1) td = klass(td, name="dt") result = td.value_counts() expected_s = Series([6], index=[Timedelta("1day")], name="dt") tm.assert_series_equal(result, expected_s) expected = TimedeltaIndex(["1 days"], name="dt") if isinstance(td, Index): tm.assert_index_equal(td.unique(), expected) else: tm.assert_numpy_array_equal(td.unique(), expected.values) td2 = timedelta(1) + (df.dt - df.dt) td2 = klass(td2, name="dt") result2 = td2.value_counts() tm.assert_series_equal(result2, expected_s) def test_factorize(self): for orig in self.objs: o = orig.copy() if isinstance(o, Index) and o.is_boolean(): exp_arr = np.array([0, 1] + [0] * 8, dtype=np.intp) exp_uniques = o exp_uniques = Index([False, True]) else: exp_arr = np.array(range(len(o)), dtype=np.intp) exp_uniques = o codes, uniques = o.factorize() tm.assert_numpy_array_equal(codes, exp_arr) if isinstance(o, Series): tm.assert_index_equal(uniques, Index(orig), check_names=False) else: # factorize explicitly resets name tm.assert_index_equal(uniques, exp_uniques, check_names=False) def test_factorize_repeated(self): for orig in self.objs: o = orig.copy() # don't test boolean if isinstance(o, Index) and o.is_boolean(): continue # sort by value, and create duplicates if isinstance(o, Series): o = o.sort_values() n = o.iloc[5:].append(o) else: indexer = o.argsort() o = o.take(indexer) n = o[5:].append(o) exp_arr = np.array( [5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp ) codes, uniques = n.factorize(sort=True) tm.assert_numpy_array_equal(codes, exp_arr) if isinstance(o, Series): tm.assert_index_equal( uniques, Index(orig).sort_values(), check_names=False ) else: tm.assert_index_equal(uniques, o, check_names=False) exp_arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4], np.intp) codes, uniques = n.factorize(sort=False) tm.assert_numpy_array_equal(codes, exp_arr) if isinstance(o, Series): expected = Index(o.iloc[5:10].append(o.iloc[:5])) tm.assert_index_equal(uniques, expected, check_names=False) else: expected = o[5:10].append(o[:5]) tm.assert_index_equal(uniques, expected, check_names=False) def test_duplicated_drop_duplicates_index(self): # GH 4060 for original in self.objs: if isinstance(original, Index): # special case if original.is_boolean(): result = original.drop_duplicates() expected = Index([False, True], name="a") tm.assert_index_equal(result, expected) continue # original doesn't have duplicates expected = np.array([False] * len(original), dtype=bool) duplicated = original.duplicated() tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = original.drop_duplicates() tm.assert_index_equal(result, original) assert result is not original # has_duplicates assert not original.has_duplicates # create repeated values, 3rd and 5th values are duplicated idx = original[list(range(len(original))) + [5, 3]] expected = np.array([False] * len(original) + [True, True], dtype=bool) duplicated = idx.duplicated() tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool tm.assert_index_equal(idx.drop_duplicates(), original) base = [False] * len(idx) base[3] = True base[5] = True expected = np.array(base) duplicated = idx.duplicated(keep="last") tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = idx.drop_duplicates(keep="last") tm.assert_index_equal(result, idx[~expected]) base = [False] * len(original) + [True, True] base[3] = True base[5] = True expected = np.array(base) duplicated = idx.duplicated(keep=False) tm.assert_numpy_array_equal(duplicated, expected) assert duplicated.dtype == bool result = idx.drop_duplicates(keep=False) tm.assert_index_equal(result, idx[~expected]) with pytest.raises( TypeError, match=r"drop_duplicates\(\) got an unexpected keyword argument", ): idx.drop_duplicates(inplace=True) else: expected = Series( [False] * len(original), index=original.index, name="a" ) tm.assert_series_equal(original.duplicated(), expected) result = original.drop_duplicates() tm.assert_series_equal(result, original) assert result is not original idx = original.index[list(range(len(original))) + [5, 3]] values = original._values[list(range(len(original))) + [5, 3]] s = Series(values, index=idx, name="a") expected = Series( [False] * len(original) + [True, True], index=idx, name="a" ) tm.assert_series_equal(s.duplicated(), expected) tm.assert_series_equal(s.drop_duplicates(), original) base = [False] * len(idx) base[3] = True base[5] = True expected = Series(base, index=idx, name="a") tm.assert_series_equal(s.duplicated(keep="last"), expected) tm.assert_series_equal( s.drop_duplicates(keep="last"), s[~np.array(base)] ) base = [False] * len(original) + [True, True] base[3] = True base[5] = True expected = Series(base, index=idx, name="a") tm.assert_series_equal(s.duplicated(keep=False), expected) tm.assert_series_equal( s.drop_duplicates(keep=False), s[~np.array(base)] ) s.drop_duplicates(inplace=True) tm.assert_series_equal(s, original) def test_drop_duplicates_series_vs_dataframe(self): # GH 14192 df = pd.DataFrame( { "a": [1, 1, 1, "one", "one"], "b": [2, 2, np.nan, np.nan, np.nan], "c": [3, 3, np.nan, np.nan, "three"], "d": [1, 2, 3, 4, 4], "e": [ datetime(2015, 1, 1), datetime(2015, 1, 1), datetime(2015, 2, 1), pd.NaT, pd.NaT, ], } ) for column in df.columns: for keep in ["first", "last", False]: dropped_frame = df[[column]].drop_duplicates(keep=keep) dropped_series = df[column].drop_duplicates(keep=keep) tm.assert_frame_equal(dropped_frame, dropped_series.to_frame()) def test_fillna(self): # # GH 11343 # though Index.fillna and Series.fillna has separate impl, # test here to confirm these works as the same for orig in self.objs: o = orig.copy() values = o.values # values will not be changed result = o.fillna(o.astype(object).values[0]) if isinstance(o, Index): tm.assert_index_equal(o, result) else: tm.assert_series_equal(o, result) # check shallow_copied assert o is not result for null_obj in [np.nan, None]: for orig in self.objs: o = orig.copy() klass = type(o) if not self._allow_na_ops(o): continue if needs_i8_conversion(o): values = o.astype(object).values fill_value = values[0] values[0:2] = pd.NaT else: values = o.values.copy() fill_value = o.values[0] values[0:2] = null_obj expected = [fill_value] * 2 + list(values[2:]) expected = klass(expected, dtype=orig.dtype) o = klass(values) # check values has the same dtype as the original assert o.dtype == orig.dtype result = o.fillna(fill_value) if isinstance(o, Index): tm.assert_index_equal(result, expected) else: tm.assert_series_equal(result, expected) # check shallow_copied assert o is not result @pytest.mark.skipif(PYPY, reason="not relevant for PyPy") def test_memory_usage(self): for o in self.objs: res = o.memory_usage() res_deep = o.memory_usage(deep=True) if is_object_dtype(o) or ( isinstance(o, Series) and is_object_dtype(o.index) ): # if there are objects, only deep will pick them up assert res_deep > res else: assert res == res_deep if isinstance(o, Series): assert ( o.memory_usage(index=False) + o.index.memory_usage() ) == o.memory_usage(index=True) # sys.getsizeof will call the .memory_usage with # deep=True, and add on some GC overhead diff = res_deep - sys.getsizeof(o) assert abs(diff) < 100 def test_searchsorted(self): # See gh-12238 for o in self.objs: index = np.searchsorted(o, max(o)) assert 0 <= index <= len(o) index = np.searchsorted(o, max(o), sorter=range(len(o))) assert 0 <= index <= len(o) def test_validate_bool_args(self): invalid_values = [1, "True", [1, 2, 3], 5.0] for value in invalid_values: with pytest.raises(ValueError): self.int_series.drop_duplicates(inplace=value) def test_getitem(self): for i in self.indexes: s = pd.Series(i) assert i[0] == s.iloc[0] assert i[5] == s.iloc[5] assert i[-1] == s.iloc[-1] assert i[-1] == i[9] with pytest.raises(IndexError): i[20] with pytest.raises(IndexError): s.iloc[20] @pytest.mark.parametrize("indexer_klass", [list, pd.Index]) @pytest.mark.parametrize( "indexer", [ [True] * 10, [False] * 10, [True, False, True, True, False, False, True, True, False, True], ], ) def test_bool_indexing(self, indexer_klass, indexer): # GH 22533 for idx in self.indexes: exp_idx = [i for i in range(len(indexer)) if indexer[i]] tm.assert_index_equal(idx[indexer_klass(indexer)], idx[exp_idx]) s = pd.Series(idx) tm.assert_series_equal(s[indexer_klass(indexer)], s.iloc[exp_idx]) def test_get_indexer_non_unique_dtype_mismatch(self): # GH 25459 indexes, missing = pd.Index(["A", "B"]).get_indexer_non_unique(pd.Index([0])) tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes) tm.assert_numpy_array_equal(np.array([0], dtype=np.int64), missing)