How to use index method in jest-extended

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test_base.py

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...57 tuples=MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])),58 repeats=Index([0, 0, 1, 1, 2, 2]),59 )60 self.setup_indices()61 def create_index(self):62 return Index(list("abcde"))63 def generate_index_types(self, skip_index_keys=[]):64 """65 Return a generator of the various index types, leaving66 out the ones with a key in skip_index_keys67 """68 for key, index in self.indices.items():69 if key not in skip_index_keys:70 yield key, index71 def test_can_hold_identifiers(self):72 index = self.create_index()73 key = index[0]74 assert index._can_hold_identifiers_and_holds_name(key) is True75 def test_new_axis(self):76 new_index = self.dateIndex[None, :]77 assert new_index.ndim == 278 assert isinstance(new_index, np.ndarray)79 def test_copy_and_deepcopy(self):80 new_copy2 = self.intIndex.copy(dtype=int)81 assert new_copy2.dtype.kind == "i"82 @pytest.mark.parametrize("attr", ["strIndex", "dateIndex"])83 def test_constructor_regular(self, attr):84 # regular instance creation85 index = getattr(self, attr)86 tm.assert_contains_all(index, index)87 def test_constructor_casting(self):88 # casting89 arr = np.array(self.strIndex)90 index = Index(arr)91 tm.assert_contains_all(arr, index)92 tm.assert_index_equal(self.strIndex, index)93 def test_constructor_copy(self):94 # copy95 arr = np.array(self.strIndex)96 index = Index(arr, copy=True, name="name")97 assert isinstance(index, Index)98 assert index.name == "name"99 tm.assert_numpy_array_equal(arr, index.values)100 arr[0] = "SOMEBIGLONGSTRING"101 assert index[0] != "SOMEBIGLONGSTRING"102 # what to do here?103 # arr = np.array(5.)104 # pytest.raises(Exception, arr.view, Index)105 def test_constructor_corner(self):106 # corner case107 msg = (108 r"Index\(\.\.\.\) must be called with a collection of some"109 " kind, 0 was passed"110 )111 with pytest.raises(TypeError, match=msg):112 Index(0)113 @pytest.mark.parametrize("index_vals", [[("A", 1), "B"], ["B", ("A", 1)]])114 def test_construction_list_mixed_tuples(self, index_vals):115 # see gh-10697: if we are constructing from a mixed list of tuples,116 # make sure that we are independent of the sorting order.117 index = Index(index_vals)118 assert isinstance(index, Index)119 assert not isinstance(index, MultiIndex)120 @pytest.mark.parametrize("na_value", [None, np.nan])121 @pytest.mark.parametrize("vtype", [list, tuple, iter])122 def test_construction_list_tuples_nan(self, na_value, vtype):123 # GH 18505 : valid tuples containing NaN124 values = [(1, "two"), (3.0, na_value)]125 result = Index(vtype(values))126 expected = MultiIndex.from_tuples(values)127 tm.assert_index_equal(result, expected)128 @pytest.mark.parametrize("cast_as_obj", [True, False])129 @pytest.mark.parametrize(130 "index",131 [132 pd.date_range(133 "2015-01-01 10:00",134 freq="D",135 periods=3,136 tz="US/Eastern",137 name="Green Eggs & Ham",138 ), # DTI with tz139 pd.date_range("2015-01-01 10:00", freq="D", periods=3), # DTI no tz140 pd.timedelta_range("1 days", freq="D", periods=3), # td141 pd.period_range("2015-01-01", freq="D", periods=3), # period142 ],143 )144 def test_constructor_from_index_dtlike(self, cast_as_obj, index):145 if cast_as_obj:146 result = pd.Index(index.astype(object))147 else:148 result = pd.Index(index)149 tm.assert_index_equal(result, index)150 if isinstance(index, pd.DatetimeIndex):151 assert result.tz == index.tz152 if cast_as_obj:153 # GH#23524 check that Index(dti, dtype=object) does not154 # incorrectly raise ValueError, and that nanoseconds are not155 # dropped156 index += pd.Timedelta(nanoseconds=50)157 result = pd.Index(index, dtype=object)158 assert result.dtype == np.object_159 assert list(result) == list(index)160 @pytest.mark.parametrize(161 "index,has_tz",162 [163 (164 pd.date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),165 True,166 ), # datetimetz167 (pd.timedelta_range("1 days", freq="D", periods=3), False), # td168 (pd.period_range("2015-01-01", freq="D", periods=3), False), # period169 ],170 )171 def test_constructor_from_series_dtlike(self, index, has_tz):172 result = pd.Index(pd.Series(index))173 tm.assert_index_equal(result, index)174 if has_tz:175 assert result.tz == index.tz176 @pytest.mark.parametrize("klass", [Index, DatetimeIndex])177 def test_constructor_from_series(self, klass):178 expected = DatetimeIndex(179 [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]180 )181 s = Series(182 [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]183 )184 result = klass(s)185 tm.assert_index_equal(result, expected)186 def test_constructor_from_series_freq(self):187 # GH 6273188 # create from a series, passing a freq189 dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]190 expected = DatetimeIndex(dts, freq="MS")191 s = Series(pd.to_datetime(dts))192 result = DatetimeIndex(s, freq="MS")193 tm.assert_index_equal(result, expected)194 def test_constructor_from_frame_series_freq(self):195 # GH 6273196 # create from a series, passing a freq197 dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]198 expected = DatetimeIndex(dts, freq="MS")199 df = pd.DataFrame(np.random.rand(5, 3))200 df["date"] = dts201 result = DatetimeIndex(df["date"], freq="MS")202 assert df["date"].dtype == object203 expected.name = "date"204 tm.assert_index_equal(result, expected)205 expected = pd.Series(dts, name="date")206 tm.assert_series_equal(df["date"], expected)207 # GH 6274208 # infer freq of same209 freq = pd.infer_freq(df["date"])210 assert freq == "MS"211 @pytest.mark.parametrize(212 "array",213 [214 np.arange(5),215 np.array(["a", "b", "c"]),216 date_range("2000-01-01", periods=3).values,217 ],218 )219 def test_constructor_ndarray_like(self, array):220 # GH 5460#issuecomment-44474502221 # it should be possible to convert any object that satisfies the numpy222 # ndarray interface directly into an Index223 class ArrayLike:224 def __init__(self, array):225 self.array = array226 def __array__(self, dtype=None):227 return self.array228 expected = pd.Index(array)229 result = pd.Index(ArrayLike(array))230 tm.assert_index_equal(result, expected)231 @pytest.mark.parametrize(232 "dtype",233 [int, "int64", "int32", "int16", "int8", "uint64", "uint32", "uint16", "uint8"],234 )235 def test_constructor_int_dtype_float(self, dtype):236 # GH 18400237 if is_unsigned_integer_dtype(dtype):238 index_type = UInt64Index239 else:240 index_type = Int64Index241 expected = index_type([0, 1, 2, 3])242 result = Index([0.0, 1.0, 2.0, 3.0], dtype=dtype)243 tm.assert_index_equal(result, expected)244 def test_constructor_int_dtype_nan(self):245 # see gh-15187246 data = [np.nan]247 expected = Float64Index(data)248 result = Index(data, dtype="float")249 tm.assert_index_equal(result, expected)250 @pytest.mark.parametrize("dtype", ["int64", "uint64"])251 def test_constructor_int_dtype_nan_raises(self, dtype):252 # see gh-15187253 data = [np.nan]254 msg = "cannot convert"255 with pytest.raises(ValueError, match=msg):256 Index(data, dtype=dtype)257 def test_constructor_no_pandas_array(self):258 ser = pd.Series([1, 2, 3])259 result = pd.Index(ser.array)260 expected = pd.Index([1, 2, 3])261 tm.assert_index_equal(result, expected)262 @pytest.mark.parametrize(263 "klass,dtype,na_val",264 [265 (pd.Float64Index, np.float64, np.nan),266 (pd.DatetimeIndex, "datetime64[ns]", pd.NaT),267 ],268 )269 def test_index_ctor_infer_nan_nat(self, klass, dtype, na_val):270 # GH 13467271 na_list = [na_val, na_val]272 expected = klass(na_list)273 assert expected.dtype == dtype274 result = Index(na_list)275 tm.assert_index_equal(result, expected)276 result = Index(np.array(na_list))277 tm.assert_index_equal(result, expected)278 @pytest.mark.parametrize("pos", [0, 1])279 @pytest.mark.parametrize(280 "klass,dtype,ctor",281 [282 (pd.DatetimeIndex, "datetime64[ns]", np.datetime64("nat")),283 (pd.TimedeltaIndex, "timedelta64[ns]", np.timedelta64("nat")),284 ],285 )286 def test_index_ctor_infer_nat_dt_like(self, pos, klass, dtype, ctor, nulls_fixture):287 expected = klass([pd.NaT, pd.NaT])288 assert expected.dtype == dtype289 data = [ctor]290 data.insert(pos, nulls_fixture)291 result = Index(data)292 tm.assert_index_equal(result, expected)293 result = Index(np.array(data, dtype=object))294 tm.assert_index_equal(result, expected)295 @pytest.mark.parametrize("swap_objs", [True, False])296 def test_index_ctor_nat_result(self, swap_objs):297 # mixed np.datetime64/timedelta64 nat results in object298 data = [np.datetime64("nat"), np.timedelta64("nat")]299 if swap_objs:300 data = data[::-1]301 expected = pd.Index(data, dtype=object)302 tm.assert_index_equal(Index(data), expected)303 tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)304 def test_index_ctor_infer_periodindex(self):305 xp = period_range("2012-1-1", freq="M", periods=3)306 rs = Index(xp)307 tm.assert_index_equal(rs, xp)308 assert isinstance(rs, PeriodIndex)309 @pytest.mark.parametrize(310 "vals,dtype",311 [312 ([1, 2, 3, 4, 5], "int"),313 ([1.1, np.nan, 2.2, 3.0], "float"),314 (["A", "B", "C", np.nan], "obj"),315 ],316 )317 def test_constructor_simple_new(self, vals, dtype):318 index = Index(vals, name=dtype)319 result = index._simple_new(index.values, dtype)320 tm.assert_index_equal(result, index)321 @pytest.mark.parametrize(322 "vals",323 [324 [1, 2, 3],325 np.array([1, 2, 3]),326 np.array([1, 2, 3], dtype=int),327 # below should coerce328 [1.0, 2.0, 3.0],329 np.array([1.0, 2.0, 3.0], dtype=float),330 ],331 )332 def test_constructor_dtypes_to_int64(self, vals):333 index = Index(vals, dtype=int)334 assert isinstance(index, Int64Index)335 @pytest.mark.parametrize(336 "vals",337 [338 [1, 2, 3],339 [1.0, 2.0, 3.0],340 np.array([1.0, 2.0, 3.0]),341 np.array([1, 2, 3], dtype=int),342 np.array([1.0, 2.0, 3.0], dtype=float),343 ],344 )345 def test_constructor_dtypes_to_float64(self, vals):346 index = Index(vals, dtype=float)347 assert isinstance(index, Float64Index)348 @pytest.mark.parametrize("cast_index", [True, False])349 @pytest.mark.parametrize(350 "vals", [[True, False, True], np.array([True, False, True], dtype=bool)]351 )352 def test_constructor_dtypes_to_object(self, cast_index, vals):353 if cast_index:354 index = Index(vals, dtype=bool)355 else:356 index = Index(vals)357 assert isinstance(index, Index)358 assert index.dtype == object359 @pytest.mark.parametrize(360 "vals",361 [362 [1, 2, 3],363 np.array([1, 2, 3], dtype=int),364 np.array(365 [np_datetime64_compat("2011-01-01"), np_datetime64_compat("2011-01-02")]366 ),367 [datetime(2011, 1, 1), datetime(2011, 1, 2)],368 ],369 )370 def test_constructor_dtypes_to_categorical(self, vals):371 index = Index(vals, dtype="category")372 assert isinstance(index, CategoricalIndex)373 @pytest.mark.parametrize("cast_index", [True, False])374 @pytest.mark.parametrize(375 "vals",376 [377 Index(378 np.array(379 [380 np_datetime64_compat("2011-01-01"),381 np_datetime64_compat("2011-01-02"),382 ]383 )384 ),385 Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),386 ],387 )388 def test_constructor_dtypes_to_datetime(self, cast_index, vals):389 if cast_index:390 index = Index(vals, dtype=object)391 assert isinstance(index, Index)392 assert index.dtype == object393 else:394 index = Index(vals)395 assert isinstance(index, DatetimeIndex)396 @pytest.mark.parametrize("cast_index", [True, False])397 @pytest.mark.parametrize(398 "vals",399 [400 np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),401 [timedelta(1), timedelta(1)],402 ],403 )404 def test_constructor_dtypes_to_timedelta(self, cast_index, vals):405 if cast_index:406 index = Index(vals, dtype=object)407 assert isinstance(index, Index)408 assert index.dtype == object409 else:410 index = Index(vals)411 assert isinstance(index, TimedeltaIndex)412 @pytest.mark.parametrize("attr, utc", [["values", False], ["asi8", True]])413 @pytest.mark.parametrize("klass", [pd.Index, pd.DatetimeIndex])414 def test_constructor_dtypes_datetime(self, tz_naive_fixture, attr, utc, klass):415 # Test constructing with a datetimetz dtype416 # .values produces numpy datetimes, so these are considered naive417 # .asi8 produces integers, so these are considered epoch timestamps418 # ^the above will be true in a later version. Right now we `.view`419 # the i8 values as NS_DTYPE, effectively treating them as wall times.420 index = pd.date_range("2011-01-01", periods=5)421 arg = getattr(index, attr)422 index = index.tz_localize(tz_naive_fixture)423 dtype = index.dtype424 if (425 tz_naive_fixture426 and attr == "asi8"427 and str(tz_naive_fixture) not in ("UTC", "tzutc()", "UTC+00:00")428 ):429 ex_warn = FutureWarning430 else:431 ex_warn = None432 # stacklevel is checked elsewhere. We don't do it here since433 # Index will have an frame, throwing off the expected.434 with tm.assert_produces_warning(ex_warn, check_stacklevel=False):435 result = klass(arg, tz=tz_naive_fixture)436 tm.assert_index_equal(result, index)437 with tm.assert_produces_warning(ex_warn, check_stacklevel=False):438 result = klass(arg, dtype=dtype)439 tm.assert_index_equal(result, index)440 with tm.assert_produces_warning(ex_warn, check_stacklevel=False):441 result = klass(list(arg), tz=tz_naive_fixture)442 tm.assert_index_equal(result, index)443 with tm.assert_produces_warning(ex_warn, check_stacklevel=False):444 result = klass(list(arg), dtype=dtype)445 tm.assert_index_equal(result, index)446 @pytest.mark.parametrize("attr", ["values", "asi8"])447 @pytest.mark.parametrize("klass", [pd.Index, pd.TimedeltaIndex])448 def test_constructor_dtypes_timedelta(self, attr, klass):449 index = pd.timedelta_range("1 days", periods=5)450 dtype = index.dtype451 values = getattr(index, attr)452 result = klass(values, dtype=dtype)453 tm.assert_index_equal(result, index)454 result = klass(list(values), dtype=dtype)455 tm.assert_index_equal(result, index)456 @pytest.mark.parametrize("value", [[], iter([]), (x for x in [])])457 @pytest.mark.parametrize(458 "klass",459 [460 Index,461 Float64Index,462 Int64Index,463 UInt64Index,464 CategoricalIndex,465 DatetimeIndex,466 TimedeltaIndex,467 ],468 )469 def test_constructor_empty(self, value, klass):470 empty = klass(value)471 assert isinstance(empty, klass)472 assert not len(empty)473 @pytest.mark.parametrize(474 "empty,klass",475 [476 (PeriodIndex([], freq="B"), PeriodIndex),477 (PeriodIndex(iter([]), freq="B"), PeriodIndex),478 (PeriodIndex((x for x in []), freq="B"), PeriodIndex),479 (RangeIndex(step=1), pd.RangeIndex),480 (MultiIndex(levels=[[1, 2], ["blue", "red"]], codes=[[], []]), MultiIndex),481 ],482 )483 def test_constructor_empty_special(self, empty, klass):484 assert isinstance(empty, klass)485 assert not len(empty)486 def test_constructor_overflow_int64(self):487 # see gh-15832488 msg = (489 "The elements provided in the data cannot "490 "all be casted to the dtype int64"491 )492 with pytest.raises(OverflowError, match=msg):493 Index([np.iinfo(np.uint64).max - 1], dtype="int64")494 @pytest.mark.xfail(reason="see GH#21311: Index doesn't enforce dtype argument")495 def test_constructor_cast(self):496 msg = "could not convert string to float"497 with pytest.raises(ValueError, match=msg):498 Index(["a", "b", "c"], dtype=float)499 def test_view_with_args(self):500 restricted = ["unicodeIndex", "strIndex", "catIndex", "boolIndex", "empty"]501 for i in list(set(self.indices.keys()) - set(restricted)):502 ind = self.indices[i]503 ind.view("i8")504 @pytest.mark.parametrize(505 "index_type",506 [507 "unicodeIndex",508 "strIndex",509 pytest.param("catIndex", marks=pytest.mark.xfail(reason="gh-25464")),510 "boolIndex",511 "empty",512 ],513 )514 def test_view_with_args_object_array_raises(self, index_type):515 ind = self.indices[index_type]516 msg = "Cannot change data-type for object array"517 with pytest.raises(TypeError, match=msg):518 ind.view("i8")519 def test_astype(self):520 casted = self.intIndex.astype("i8")521 # it works!522 casted.get_loc(5)523 # pass on name524 self.intIndex.name = "foobar"525 casted = self.intIndex.astype("i8")526 assert casted.name == "foobar"527 def test_equals_object(self):528 # same529 assert Index(["a", "b", "c"]).equals(Index(["a", "b", "c"]))530 @pytest.mark.parametrize(531 "comp", [Index(["a", "b"]), Index(["a", "b", "d"]), ["a", "b", "c"]]532 )533 def test_not_equals_object(self, comp):534 assert not Index(["a", "b", "c"]).equals(comp)535 def test_insert(self):536 # GH 7256537 # validate neg/pos inserts538 result = Index(["b", "c", "d"])539 # test 0th element540 tm.assert_index_equal(Index(["a", "b", "c", "d"]), result.insert(0, "a"))541 # test Nth element that follows Python list behavior542 tm.assert_index_equal(Index(["b", "c", "e", "d"]), result.insert(-1, "e"))543 # test loc +/- neq (0, -1)544 tm.assert_index_equal(result.insert(1, "z"), result.insert(-2, "z"))545 # test empty546 null_index = Index([])547 tm.assert_index_equal(Index(["a"]), null_index.insert(0, "a"))548 def test_insert_missing(self, nulls_fixture):549 # GH 22295550 # test there is no mangling of NA values551 expected = Index(["a", nulls_fixture, "b", "c"])552 result = Index(list("abc")).insert(1, nulls_fixture)553 tm.assert_index_equal(result, expected)554 @pytest.mark.parametrize(555 "pos,expected",556 [557 (0, Index(["b", "c", "d"], name="index")),558 (-1, Index(["a", "b", "c"], name="index")),559 ],560 )561 def test_delete(self, pos, expected):562 index = Index(["a", "b", "c", "d"], name="index")563 result = index.delete(pos)564 tm.assert_index_equal(result, expected)565 assert result.name == expected.name566 def test_delete_raises(self):567 index = Index(["a", "b", "c", "d"], name="index")568 msg = "index 5 is out of bounds for axis 0 with size 4"569 with pytest.raises(IndexError, match=msg):570 index.delete(5)571 def test_identical(self):572 # index573 i1 = Index(["a", "b", "c"])574 i2 = Index(["a", "b", "c"])575 assert i1.identical(i2)576 i1 = i1.rename("foo")577 assert i1.equals(i2)578 assert not i1.identical(i2)579 i2 = i2.rename("foo")580 assert i1.identical(i2)581 i3 = Index([("a", "a"), ("a", "b"), ("b", "a")])582 i4 = Index([("a", "a"), ("a", "b"), ("b", "a")], tupleize_cols=False)583 assert not i3.identical(i4)584 def test_is_(self):585 ind = Index(range(10))586 assert ind.is_(ind)587 assert ind.is_(ind.view().view().view().view())588 assert not ind.is_(Index(range(10)))589 assert not ind.is_(ind.copy())590 assert not ind.is_(ind.copy(deep=False))591 assert not ind.is_(ind[:])592 assert not ind.is_(np.array(range(10)))593 # quasi-implementation dependent594 assert ind.is_(ind.view())595 ind2 = ind.view()596 ind2.name = "bob"597 assert ind.is_(ind2)598 assert ind2.is_(ind)599 # doesn't matter if Indices are *actually* views of underlying data,600 assert not ind.is_(Index(ind.values))601 arr = np.array(range(1, 11))602 ind1 = Index(arr, copy=False)603 ind2 = Index(arr, copy=False)604 assert not ind1.is_(ind2)605 def test_asof(self):606 d = self.dateIndex[0]607 assert self.dateIndex.asof(d) == d608 assert isna(self.dateIndex.asof(d - timedelta(1)))609 d = self.dateIndex[-1]610 assert self.dateIndex.asof(d + timedelta(1)) == d611 d = self.dateIndex[0].to_pydatetime()612 assert isinstance(self.dateIndex.asof(d), Timestamp)613 def test_asof_datetime_partial(self):614 index = pd.date_range("2010-01-01", periods=2, freq="m")615 expected = Timestamp("2010-02-28")616 result = index.asof("2010-02")617 assert result == expected618 assert not isinstance(result, Index)619 def test_nanosecond_index_access(self):620 s = Series([Timestamp("20130101")]).values.view("i8")[0]621 r = DatetimeIndex([s + 50 + i for i in range(100)])622 x = Series(np.random.randn(100), index=r)623 first_value = x.asof(x.index[0])624 # this does not yet work, as parsing strings is done via dateutil625 # assert first_value == x['2013-01-01 00:00:00.000000050+0000']626 expected_ts = np_datetime64_compat("2013-01-01 00:00:00.000000050+0000", "ns")627 assert first_value == x[Timestamp(expected_ts)]628 def test_booleanindex(self):629 boolIndex = np.repeat(True, len(self.strIndex)).astype(bool)630 boolIndex[5:30:2] = False631 subIndex = self.strIndex[boolIndex]632 for i, val in enumerate(subIndex):633 assert subIndex.get_loc(val) == i634 subIndex = self.strIndex[list(boolIndex)]635 for i, val in enumerate(subIndex):636 assert subIndex.get_loc(val) == i637 def test_fancy(self):638 sl = self.strIndex[[1, 2, 3]]639 for i in sl:640 assert i == sl[sl.get_loc(i)]641 @pytest.mark.parametrize("attr", ["strIndex", "intIndex", "floatIndex"])642 @pytest.mark.parametrize("dtype", [np.int_, np.bool_])643 def test_empty_fancy(self, attr, dtype):644 empty_arr = np.array([], dtype=dtype)645 index = getattr(self, attr)646 empty_index = index.__class__([])647 assert index[[]].identical(empty_index)648 assert index[empty_arr].identical(empty_index)649 @pytest.mark.parametrize("attr", ["strIndex", "intIndex", "floatIndex"])650 def test_empty_fancy_raises(self, attr):651 # pd.DatetimeIndex is excluded, because it overrides getitem and should652 # be tested separately.653 empty_farr = np.array([], dtype=np.float_)654 index = getattr(self, attr)655 empty_index = index.__class__([])656 assert index[[]].identical(empty_index)657 # np.ndarray only accepts ndarray of int & bool dtypes, so should Index658 msg = r"arrays used as indices must be of integer \(or boolean\) type"659 with pytest.raises(IndexError, match=msg):660 index[empty_farr]661 @pytest.mark.parametrize("sort", [None, False])662 def test_intersection(self, sort):663 first = self.strIndex[:20]664 second = self.strIndex[:10]665 intersect = first.intersection(second, sort=sort)666 if sort is None:667 tm.assert_index_equal(intersect, second.sort_values())668 assert tm.equalContents(intersect, second)669 # Corner cases670 inter = first.intersection(first, sort=sort)671 assert inter is first672 @pytest.mark.parametrize(673 "index2,keeps_name",674 [675 (Index([3, 4, 5, 6, 7], name="index"), True), # preserve same name676 (Index([3, 4, 5, 6, 7], name="other"), False), # drop diff names677 (Index([3, 4, 5, 6, 7]), False),678 ],679 )680 @pytest.mark.parametrize("sort", [None, False])681 def test_intersection_name_preservation(self, index2, keeps_name, sort):682 index1 = Index([1, 2, 3, 4, 5], name="index")683 expected = Index([3, 4, 5])684 result = index1.intersection(index2, sort)685 if keeps_name:686 expected.name = "index"687 assert result.name == expected.name688 tm.assert_index_equal(result, expected)689 @pytest.mark.parametrize(690 "first_name,second_name,expected_name",691 [("A", "A", "A"), ("A", "B", None), (None, "B", None)],692 )693 @pytest.mark.parametrize("sort", [None, False])694 def test_intersection_name_preservation2(695 self, first_name, second_name, expected_name, sort696 ):697 first = self.strIndex[5:20]698 second = self.strIndex[:10]699 first.name = first_name700 second.name = second_name701 intersect = first.intersection(second, sort=sort)702 assert intersect.name == expected_name703 @pytest.mark.parametrize(704 "index2,keeps_name",705 [706 (Index([4, 7, 6, 5, 3], name="index"), True),707 (Index([4, 7, 6, 5, 3], name="other"), False),708 ],709 )710 @pytest.mark.parametrize("sort", [None, False])711 def test_intersection_monotonic(self, index2, keeps_name, sort):712 index1 = Index([5, 3, 2, 4, 1], name="index")713 expected = Index([5, 3, 4])714 if keeps_name:715 expected.name = "index"716 result = index1.intersection(index2, sort=sort)717 if sort is None:718 expected = expected.sort_values()719 tm.assert_index_equal(result, expected)720 @pytest.mark.parametrize(721 "index2,expected_arr",722 [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B", "A"])],723 )724 @pytest.mark.parametrize("sort", [None, False])725 def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):726 # non-monotonic non-unique727 index1 = Index(["A", "B", "A", "C"])728 expected = Index(expected_arr, dtype="object")729 result = index1.intersection(index2, sort=sort)730 if sort is None:731 expected = expected.sort_values()732 tm.assert_index_equal(result, expected)733 @pytest.mark.parametrize("sort", [None, False])734 def test_intersect_str_dates(self, sort):735 dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]736 i1 = Index(dt_dates, dtype=object)737 i2 = Index(["aa"], dtype=object)738 result = i2.intersection(i1, sort=sort)739 assert len(result) == 0740 def test_intersect_nosort(self):741 result = pd.Index(["c", "b", "a"]).intersection(["b", "a"])742 expected = pd.Index(["b", "a"])743 tm.assert_index_equal(result, expected)744 def test_intersection_equal_sort(self):745 idx = pd.Index(["c", "a", "b"])746 tm.assert_index_equal(idx.intersection(idx, sort=False), idx)747 tm.assert_index_equal(idx.intersection(idx, sort=None), idx)748 @pytest.mark.xfail(reason="Not implemented")749 def test_intersection_equal_sort_true(self):750 # TODO decide on True behaviour751 idx = pd.Index(["c", "a", "b"])752 sorted_ = pd.Index(["a", "b", "c"])753 tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)754 @pytest.mark.parametrize("sort", [None, False])755 def test_chained_union(self, sort):756 # Chained unions handles names correctly757 i1 = Index([1, 2], name="i1")758 i2 = Index([5, 6], name="i2")759 i3 = Index([3, 4], name="i3")760 union = i1.union(i2.union(i3, sort=sort), sort=sort)761 expected = i1.union(i2, sort=sort).union(i3, sort=sort)762 tm.assert_index_equal(union, expected)763 j1 = Index([1, 2], name="j1")764 j2 = Index([], name="j2")765 j3 = Index([], name="j3")766 union = j1.union(j2.union(j3, sort=sort), sort=sort)767 expected = j1.union(j2, sort=sort).union(j3, sort=sort)768 tm.assert_index_equal(union, expected)769 @pytest.mark.parametrize("sort", [None, False])770 def test_union(self, sort):771 # TODO: Replace with fixturesult772 first = self.strIndex[5:20]773 second = self.strIndex[:10]774 everything = self.strIndex[:20]775 union = first.union(second, sort=sort)776 if sort is None:777 tm.assert_index_equal(union, everything.sort_values())778 assert tm.equalContents(union, everything)779 @pytest.mark.parametrize("slice_", [slice(None), slice(0)])780 def test_union_sort_other_special(self, slice_):781 # https://github.com/pandas-dev/pandas/issues/24959782 idx = pd.Index([1, 0, 2])783 # default, sort=None784 other = idx[slice_]785 tm.assert_index_equal(idx.union(other), idx)786 tm.assert_index_equal(other.union(idx), idx)787 # sort=False788 tm.assert_index_equal(idx.union(other, sort=False), idx)789 @pytest.mark.xfail(reason="Not implemented")790 @pytest.mark.parametrize("slice_", [slice(None), slice(0)])791 def test_union_sort_special_true(self, slice_):792 # TODO decide on True behaviour793 # sort=True794 idx = pd.Index([1, 0, 2])795 # default, sort=None796 other = idx[slice_]797 result = idx.union(other, sort=True)798 expected = pd.Index([0, 1, 2])799 tm.assert_index_equal(result, expected)800 def test_union_sort_other_incomparable(self):801 # https://github.com/pandas-dev/pandas/issues/24959802 idx = pd.Index([1, pd.Timestamp("2000")])803 # default (sort=None)804 with tm.assert_produces_warning(RuntimeWarning):805 result = idx.union(idx[:1])806 tm.assert_index_equal(result, idx)807 # sort=None808 with tm.assert_produces_warning(RuntimeWarning):809 result = idx.union(idx[:1], sort=None)810 tm.assert_index_equal(result, idx)811 # sort=False812 result = idx.union(idx[:1], sort=False)813 tm.assert_index_equal(result, idx)814 @pytest.mark.xfail(reason="Not implemented")815 def test_union_sort_other_incomparable_true(self):816 # TODO decide on True behaviour817 # sort=True818 idx = pd.Index([1, pd.Timestamp("2000")])819 with pytest.raises(TypeError, match=".*"):820 idx.union(idx[:1], sort=True)821 @pytest.mark.parametrize("klass", [np.array, Series, list])822 @pytest.mark.parametrize("sort", [None, False])823 def test_union_from_iterables(self, klass, sort):824 # GH 10149825 # TODO: Replace with fixturesult826 first = self.strIndex[5:20]827 second = self.strIndex[:10]828 everything = self.strIndex[:20]829 case = klass(second.values)830 result = first.union(case, sort=sort)831 if sort is None:832 tm.assert_index_equal(result, everything.sort_values())833 assert tm.equalContents(result, everything)834 @pytest.mark.parametrize("sort", [None, False])835 def test_union_identity(self, sort):836 # TODO: replace with fixturesult837 first = self.strIndex[5:20]838 union = first.union(first, sort=sort)839 # i.e. identity is not preserved when sort is True840 assert (union is first) is (not sort)841 # This should no longer be the same object, since [] is not consistent,842 # both objects will be recast to dtype('O')843 union = first.union([], sort=sort)844 assert (union is first) is (not sort)845 union = Index([]).union(first, sort=sort)846 assert (union is first) is (not sort)847 @pytest.mark.parametrize("first_list", [list("ba"), list()])848 @pytest.mark.parametrize("second_list", [list("ab"), list()])849 @pytest.mark.parametrize(850 "first_name, second_name, expected_name",851 [("A", "B", None), (None, "B", None), ("A", None, None)],852 )853 @pytest.mark.parametrize("sort", [None, False])854 def test_union_name_preservation(855 self, first_list, second_list, first_name, second_name, expected_name, sort856 ):857 first = Index(first_list, name=first_name)858 second = Index(second_list, name=second_name)859 union = first.union(second, sort=sort)860 vals = set(first_list).union(second_list)861 if sort is None and len(first_list) > 0 and len(second_list) > 0:862 expected = Index(sorted(vals), name=expected_name)863 tm.assert_index_equal(union, expected)864 else:865 expected = Index(vals, name=expected_name)866 assert tm.equalContents(union, expected)867 @pytest.mark.parametrize("sort", [None, False])868 def test_union_dt_as_obj(self, sort):869 # TODO: Replace with fixturesult870 firstCat = self.strIndex.union(self.dateIndex)871 secondCat = self.strIndex.union(self.strIndex)872 if self.dateIndex.dtype == np.object_:873 appended = np.append(self.strIndex, self.dateIndex)874 else:875 appended = np.append(self.strIndex, self.dateIndex.astype("O"))876 assert tm.equalContents(firstCat, appended)877 assert tm.equalContents(secondCat, self.strIndex)878 tm.assert_contains_all(self.strIndex, firstCat)879 tm.assert_contains_all(self.strIndex, secondCat)880 tm.assert_contains_all(self.dateIndex, firstCat)881 @pytest.mark.parametrize(882 "method", ["union", "intersection", "difference", "symmetric_difference"]883 )884 def test_setops_disallow_true(self, method):885 idx1 = pd.Index(["a", "b"])886 idx2 = pd.Index(["b", "c"])887 with pytest.raises(ValueError, match="The 'sort' keyword only takes"):888 getattr(idx1, method)(idx2, sort=True)889 def test_map_identity_mapping(self):890 # GH 12766891 # TODO: replace with fixture892 for name, cur_index in self.indices.items():893 tm.assert_index_equal(cur_index, cur_index.map(lambda x: x))894 def test_map_with_tuples(self):895 # GH 12766896 # Test that returning a single tuple from an Index897 # returns an Index.898 index = tm.makeIntIndex(3)899 result = tm.makeIntIndex(3).map(lambda x: (x,))900 expected = Index([(i,) for i in index])901 tm.assert_index_equal(result, expected)902 # Test that returning a tuple from a map of a single index903 # returns a MultiIndex object.904 result = index.map(lambda x: (x, x == 1))905 expected = MultiIndex.from_tuples([(i, i == 1) for i in index])906 tm.assert_index_equal(result, expected)907 def test_map_with_tuples_mi(self):908 # Test that returning a single object from a MultiIndex909 # returns an Index.910 first_level = ["foo", "bar", "baz"]911 multi_index = MultiIndex.from_tuples(zip(first_level, [1, 2, 3]))912 reduced_index = multi_index.map(lambda x: x[0])913 tm.assert_index_equal(reduced_index, Index(first_level))914 @pytest.mark.parametrize(915 "attr", ["makeDateIndex", "makePeriodIndex", "makeTimedeltaIndex"]916 )917 def test_map_tseries_indices_return_index(self, attr):918 index = getattr(tm, attr)(10)919 expected = Index([1] * 10)920 result = index.map(lambda x: 1)921 tm.assert_index_equal(expected, result)922 def test_map_tseries_indices_accsr_return_index(self):923 date_index = tm.makeDateIndex(24, freq="h", name="hourly")924 expected = Index(range(24), name="hourly")925 tm.assert_index_equal(expected, date_index.map(lambda x: x.hour))926 @pytest.mark.parametrize(927 "mapper",928 [929 lambda values, index: {i: e for e, i in zip(values, index)},930 lambda values, index: pd.Series(values, index),931 ],932 )933 def test_map_dictlike(self, mapper):934 # GH 12756935 expected = Index(["foo", "bar", "baz"])936 index = tm.makeIntIndex(3)937 result = index.map(mapper(expected.values, index))938 tm.assert_index_equal(result, expected)939 # TODO: replace with fixture940 for name in self.indices.keys():941 if name == "catIndex":942 # Tested in test_categorical943 continue944 elif name == "repeats":945 # Cannot map duplicated index946 continue947 index = self.indices[name]948 expected = Index(np.arange(len(index), 0, -1))949 # to match proper result coercion for uints950 if name == "empty":951 expected = Index([])952 result = index.map(mapper(expected, index))953 tm.assert_index_equal(result, expected)954 @pytest.mark.parametrize(955 "mapper",956 [Series(["foo", 2.0, "baz"], index=[0, 2, -1]), {0: "foo", 2: 2.0, -1: "baz"}],957 )958 def test_map_with_non_function_missing_values(self, mapper):959 # GH 12756960 expected = Index([2.0, np.nan, "foo"])961 result = Index([2, 1, 0]).map(mapper)962 tm.assert_index_equal(expected, result)963 def test_map_na_exclusion(self):964 index = Index([1.5, np.nan, 3, np.nan, 5])965 result = index.map(lambda x: x * 2, na_action="ignore")966 expected = index * 2967 tm.assert_index_equal(result, expected)968 def test_map_defaultdict(self):969 index = Index([1, 2, 3])970 default_dict = defaultdict(lambda: "blank")971 default_dict[1] = "stuff"972 result = index.map(default_dict)973 expected = Index(["stuff", "blank", "blank"])974 tm.assert_index_equal(result, expected)975 def test_append_multiple(self):976 index = Index(["a", "b", "c", "d", "e", "f"])977 foos = [index[:2], index[2:4], index[4:]]978 result = foos[0].append(foos[1:])979 tm.assert_index_equal(result, index)980 # empty981 result = index.append([])982 tm.assert_index_equal(result, index)983 @pytest.mark.parametrize("name,expected", [("foo", "foo"), ("bar", None)])984 def test_append_empty_preserve_name(self, name, expected):985 left = Index([], name="foo")986 right = Index([1, 2, 3], name=name)987 result = left.append(right)988 assert result.name == expected989 @pytest.mark.parametrize("second_name,expected", [(None, None), ("name", "name")])990 @pytest.mark.parametrize("sort", [None, False])991 def test_difference_name_preservation(self, second_name, expected, sort):992 # TODO: replace with fixturesult993 first = self.strIndex[5:20]994 second = self.strIndex[:10]995 answer = self.strIndex[10:20]996 first.name = "name"997 second.name = second_name998 result = first.difference(second, sort=sort)999 assert tm.equalContents(result, answer)1000 if expected is None:1001 assert result.name is None1002 else:1003 assert result.name == expected1004 @pytest.mark.parametrize("sort", [None, False])1005 def test_difference_empty_arg(self, sort):1006 first = self.strIndex[5:20]1007 first.name == "name"1008 result = first.difference([], sort)1009 assert tm.equalContents(result, first)1010 assert result.name == first.name1011 @pytest.mark.parametrize("sort", [None, False])1012 def test_difference_identity(self, sort):1013 first = self.strIndex[5:20]1014 first.name == "name"1015 result = first.difference(first, sort)1016 assert len(result) == 01017 assert result.name == first.name1018 @pytest.mark.parametrize("sort", [None, False])1019 def test_difference_sort(self, sort):1020 first = self.strIndex[5:20]1021 second = self.strIndex[:10]1022 result = first.difference(second, sort)1023 expected = self.strIndex[10:20]1024 if sort is None:1025 expected = expected.sort_values()1026 tm.assert_index_equal(result, expected)1027 @pytest.mark.parametrize("sort", [None, False])1028 def test_symmetric_difference(self, sort):1029 # smoke1030 index1 = Index([5, 2, 3, 4], name="index1")1031 index2 = Index([2, 3, 4, 1])1032 result = index1.symmetric_difference(index2, sort=sort)1033 expected = Index([5, 1])1034 assert tm.equalContents(result, expected)1035 assert result.name is None1036 if sort is None:1037 expected = expected.sort_values()1038 tm.assert_index_equal(result, expected)1039 # __xor__ syntax1040 expected = index1 ^ index21041 assert tm.equalContents(result, expected)1042 assert result.name is None1043 @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])1044 def test_difference_incomparable(self, opname):1045 a = pd.Index([3, pd.Timestamp("2000"), 1])1046 b = pd.Index([2, pd.Timestamp("1999"), 1])1047 op = operator.methodcaller(opname, b)1048 # sort=None, the default1049 result = op(a)1050 expected = pd.Index([3, pd.Timestamp("2000"), 2, pd.Timestamp("1999")])1051 if opname == "difference":1052 expected = expected[:2]1053 tm.assert_index_equal(result, expected)1054 # sort=False1055 op = operator.methodcaller(opname, b, sort=False)1056 result = op(a)1057 tm.assert_index_equal(result, expected)1058 @pytest.mark.xfail(reason="Not implemented")1059 @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])1060 def test_difference_incomparable_true(self, opname):1061 # TODO decide on True behaviour1062 # # sort=True, raises1063 a = pd.Index([3, pd.Timestamp("2000"), 1])1064 b = pd.Index([2, pd.Timestamp("1999"), 1])1065 op = operator.methodcaller(opname, b, sort=True)1066 with pytest.raises(TypeError, match="Cannot compare"):1067 op(a)1068 @pytest.mark.parametrize("sort", [None, False])1069 def test_symmetric_difference_mi(self, sort):1070 index1 = MultiIndex.from_tuples(self.tuples)1071 index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])1072 result = index1.symmetric_difference(index2, sort=sort)1073 expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])1074 if sort is None:1075 expected = expected.sort_values()1076 tm.assert_index_equal(result, expected)1077 assert tm.equalContents(result, expected)1078 @pytest.mark.parametrize(1079 "index2,expected",1080 [1081 (Index([0, 1, np.nan]), Index([2.0, 3.0, 0.0])),1082 (Index([0, 1]), Index([np.nan, 2.0, 3.0, 0.0])),1083 ],1084 )1085 @pytest.mark.parametrize("sort", [None, False])1086 def test_symmetric_difference_missing(self, index2, expected, sort):1087 # GH 13514 change: {nan} - {nan} == {}1088 # (GH 6444, sorting of nans, is no longer an issue)1089 index1 = Index([1, np.nan, 2, 3])1090 result = index1.symmetric_difference(index2, sort=sort)1091 if sort is None:1092 expected = expected.sort_values()1093 tm.assert_index_equal(result, expected)1094 @pytest.mark.parametrize("sort", [None, False])1095 def test_symmetric_difference_non_index(self, sort):1096 index1 = Index([1, 2, 3, 4], name="index1")1097 index2 = np.array([2, 3, 4, 5])1098 expected = Index([1, 5])1099 result = index1.symmetric_difference(index2, sort=sort)1100 assert tm.equalContents(result, expected)1101 assert result.name == "index1"1102 result = index1.symmetric_difference(index2, result_name="new_name", sort=sort)1103 assert tm.equalContents(result, expected)1104 assert result.name == "new_name"1105 @pytest.mark.parametrize("sort", [None, False])1106 def test_difference_type(self, sort):1107 # GH 200401108 # If taking difference of a set and itself, it1109 # needs to preserve the type of the index1110 skip_index_keys = ["repeats"]1111 for key, index in self.generate_index_types(skip_index_keys):1112 result = index.difference(index, sort=sort)1113 expected = index.drop(index)1114 tm.assert_index_equal(result, expected)1115 @pytest.mark.parametrize("sort", [None, False])1116 def test_intersection_difference(self, sort):1117 # GH 200401118 # Test that the intersection of an index with an1119 # empty index produces the same index as the difference1120 # of an index with itself. Test for all types1121 skip_index_keys = ["repeats"]1122 for key, index in self.generate_index_types(skip_index_keys):1123 inter = index.intersection(index.drop(index))1124 diff = index.difference(index, sort=sort)1125 tm.assert_index_equal(inter, diff)1126 @pytest.mark.parametrize(1127 "attr,expected",1128 [1129 ("strIndex", False),1130 ("boolIndex", False),1131 ("catIndex", False),1132 ("intIndex", True),1133 ("dateIndex", False),1134 ("floatIndex", True),1135 ],1136 )1137 def test_is_numeric(self, attr, expected):1138 assert getattr(self, attr).is_numeric() == expected1139 @pytest.mark.parametrize(1140 "attr,expected",1141 [1142 ("strIndex", True),1143 ("boolIndex", True),1144 ("catIndex", False),1145 ("intIndex", False),1146 ("dateIndex", False),1147 ("floatIndex", False),1148 ],1149 )1150 def test_is_object(self, attr, expected):1151 assert getattr(self, attr).is_object() == expected1152 @pytest.mark.parametrize(1153 "attr,expected",1154 [1155 ("strIndex", False),1156 ("boolIndex", False),1157 ("catIndex", False),1158 ("intIndex", False),1159 ("dateIndex", True),1160 ("floatIndex", False),1161 ],1162 )1163 def test_is_all_dates(self, attr, expected):1164 assert getattr(self, attr).is_all_dates == expected1165 def test_summary(self):1166 self._check_method_works(Index._summary)1167 # GH38691168 ind = Index(["{other}%s", "~:{range}:0"], name="A")1169 result = ind._summary()1170 # shouldn't be formatted accidentally.1171 assert "~:{range}:0" in result1172 assert "{other}%s" in result1173 # GH182171174 def test_summary_deprecated(self):1175 ind = Index(["{other}%s", "~:{range}:0"], name="A")1176 with tm.assert_produces_warning(FutureWarning):1177 ind.summary()1178 def test_format(self):1179 self._check_method_works(Index.format)1180 # GH 146261181 # windows has different precision on datetime.datetime.now (it doesn't1182 # include us since the default for Timestamp shows these but Index1183 # formatting does not we are skipping)1184 now = datetime.now()1185 if not str(now).endswith("000"):1186 index = Index([now])1187 formatted = index.format()1188 expected = [str(index[0])]1189 assert formatted == expected1190 self.strIndex[:0].format()1191 @pytest.mark.parametrize("vals", [[1, 2.0 + 3.0j, 4.0], ["a", "b", "c"]])1192 def test_format_missing(self, vals, nulls_fixture):1193 # 28451194 vals = list(vals) # Copy for each iteration1195 vals.append(nulls_fixture)1196 index = Index(vals)1197 formatted = index.format()1198 expected = [str(index[0]), str(index[1]), str(index[2]), "NaN"]1199 assert formatted == expected1200 assert index[3] is nulls_fixture1201 def test_format_with_name_time_info(self):1202 # bug I fixed 12/20/20111203 inc = timedelta(hours=4)1204 dates = Index([dt + inc for dt in self.dateIndex], name="something")1205 formatted = dates.format(name=True)1206 assert formatted[0] == "something"1207 def test_format_datetime_with_time(self):1208 t = Index([datetime(2012, 2, 7), datetime(2012, 2, 7, 23)])1209 result = t.format()1210 expected = ["2012-02-07 00:00:00", "2012-02-07 23:00:00"]1211 assert len(result) == 21212 assert result == expected1213 @pytest.mark.parametrize("op", ["any", "all"])1214 def test_logical_compat(self, op):1215 index = self.create_index()1216 assert getattr(index, op)() == getattr(index.values, op)()1217 def _check_method_works(self, method):1218 # TODO: make this a dedicated test with parametrized methods1219 method(self.empty)1220 method(self.dateIndex)1221 method(self.unicodeIndex)1222 method(self.strIndex)1223 method(self.intIndex)1224 method(self.tuples)1225 method(self.catIndex)1226 def test_get_indexer(self):1227 index1 = Index([1, 2, 3, 4, 5])1228 index2 = Index([2, 4, 6])1229 r1 = index1.get_indexer(index2)1230 e1 = np.array([1, 3, -1], dtype=np.intp)1231 assert_almost_equal(r1, e1)1232 @pytest.mark.parametrize("reverse", [True, False])1233 @pytest.mark.parametrize(1234 "expected,method",1235 [1236 (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "pad"),1237 (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "ffill"),1238 (np.array([0, 0, 1, 1, 2], dtype=np.intp), "backfill"),1239 (np.array([0, 0, 1, 1, 2], dtype=np.intp), "bfill"),1240 ],1241 )1242 def test_get_indexer_methods(self, reverse, expected, method):1243 index1 = Index([1, 2, 3, 4, 5])1244 index2 = Index([2, 4, 6])1245 if reverse:1246 index1 = index1[::-1]1247 expected = expected[::-1]1248 result = index2.get_indexer(index1, method=method)1249 assert_almost_equal(result, expected)1250 def test_get_indexer_invalid(self):1251 # GH104111252 index = Index(np.arange(10))1253 with pytest.raises(ValueError, match="tolerance argument"):1254 index.get_indexer([1, 0], tolerance=1)1255 with pytest.raises(ValueError, match="limit argument"):1256 index.get_indexer([1, 0], limit=1)1257 @pytest.mark.parametrize(1258 "method, tolerance, indexer, expected",1259 [1260 ("pad", None, [0, 5, 9], [0, 5, 9]),1261 ("backfill", None, [0, 5, 9], [0, 5, 9]),1262 ("nearest", None, [0, 5, 9], [0, 5, 9]),1263 ("pad", 0, [0, 5, 9], [0, 5, 9]),1264 ("backfill", 0, [0, 5, 9], [0, 5, 9]),1265 ("nearest", 0, [0, 5, 9], [0, 5, 9]),1266 ("pad", None, [0.2, 1.8, 8.5], [0, 1, 8]),1267 ("backfill", None, [0.2, 1.8, 8.5], [1, 2, 9]),1268 ("nearest", None, [0.2, 1.8, 8.5], [0, 2, 9]),1269 ("pad", 1, [0.2, 1.8, 8.5], [0, 1, 8]),1270 ("backfill", 1, [0.2, 1.8, 8.5], [1, 2, 9]),1271 ("nearest", 1, [0.2, 1.8, 8.5], [0, 2, 9]),1272 ("pad", 0.2, [0.2, 1.8, 8.5], [0, -1, -1]),1273 ("backfill", 0.2, [0.2, 1.8, 8.5], [-1, 2, -1]),1274 ("nearest", 0.2, [0.2, 1.8, 8.5], [0, 2, -1]),1275 ],1276 )1277 def test_get_indexer_nearest(self, method, tolerance, indexer, expected):1278 index = Index(np.arange(10))1279 actual = index.get_indexer(indexer, method=method, tolerance=tolerance)1280 tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))1281 @pytest.mark.parametrize("listtype", [list, tuple, Series, np.array])1282 @pytest.mark.parametrize(1283 "tolerance, expected",1284 list(1285 zip(1286 [[0.3, 0.3, 0.1], [0.2, 0.1, 0.1], [0.1, 0.5, 0.5]],1287 [[0, 2, -1], [0, -1, -1], [-1, 2, 9]],1288 )1289 ),1290 )1291 def test_get_indexer_nearest_listlike_tolerance(1292 self, tolerance, expected, listtype1293 ):1294 index = Index(np.arange(10))1295 actual = index.get_indexer(1296 [0.2, 1.8, 8.5], method="nearest", tolerance=listtype(tolerance)1297 )1298 tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))1299 def test_get_indexer_nearest_error(self):1300 index = Index(np.arange(10))1301 with pytest.raises(ValueError, match="limit argument"):1302 index.get_indexer([1, 0], method="nearest", limit=1)1303 with pytest.raises(ValueError, match="tolerance size must match"):1304 index.get_indexer([1, 0], method="nearest", tolerance=[1, 2, 3])1305 @pytest.mark.parametrize(1306 "method,expected",1307 [("pad", [8, 7, 0]), ("backfill", [9, 8, 1]), ("nearest", [9, 7, 0])],1308 )1309 def test_get_indexer_nearest_decreasing(self, method, expected):1310 index = Index(np.arange(10))[::-1]1311 actual = index.get_indexer([0, 5, 9], method=method)1312 tm.assert_numpy_array_equal(actual, np.array([9, 4, 0], dtype=np.intp))1313 actual = index.get_indexer([0.2, 1.8, 8.5], method=method)1314 tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))1315 @pytest.mark.parametrize(1316 "method,expected",1317 [1318 ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),1319 ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),1320 ],1321 )1322 def test_get_indexer_strings(self, method, expected):1323 index = pd.Index(["b", "c"])1324 actual = index.get_indexer(["a", "b", "c", "d"], method=method)1325 tm.assert_numpy_array_equal(actual, expected)1326 def test_get_indexer_strings_raises(self):1327 index = pd.Index(["b", "c"])1328 msg = r"unsupported operand type\(s\) for -: 'str' and 'str'"1329 with pytest.raises(TypeError, match=msg):1330 index.get_indexer(["a", "b", "c", "d"], method="nearest")1331 with pytest.raises(TypeError, match=msg):1332 index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)1333 with pytest.raises(TypeError, match=msg):1334 index.get_indexer(1335 ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]1336 )1337 @pytest.mark.parametrize("idx_class", [Int64Index, RangeIndex, Float64Index])1338 def test_get_indexer_numeric_index_boolean_target(self, idx_class):1339 # GH 168771340 numeric_index = idx_class(RangeIndex((4)))1341 result = numeric_index.get_indexer([True, False, True])1342 expected = np.array([-1, -1, -1], dtype=np.intp)1343 tm.assert_numpy_array_equal(result, expected)1344 def test_get_indexer_with_NA_values(1345 self, unique_nulls_fixture, unique_nulls_fixture21346 ):1347 # GH 223321348 # check pairwise, that no pair of na values1349 # is mangled1350 if unique_nulls_fixture is unique_nulls_fixture2:1351 return # skip it, values are not unique1352 arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=np.object)1353 index = pd.Index(arr, dtype=np.object)1354 result = index.get_indexer(1355 [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"]1356 )1357 expected = np.array([0, 1, -1], dtype=np.intp)1358 tm.assert_numpy_array_equal(result, expected)1359 @pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])1360 def test_get_loc(self, method):1361 index = pd.Index([0, 1, 2])1362 assert index.get_loc(1, method=method) == 11363 if method:1364 assert index.get_loc(1, method=method, tolerance=0) == 11365 @pytest.mark.parametrize("method", [None, "pad", "backfill", "nearest"])1366 def test_get_loc_raises_bad_label(self, method):1367 index = pd.Index([0, 1, 2])1368 if method:1369 # Messages vary across versions1370 if PY36:1371 msg = "not supported between"1372 else:1373 msg = "unorderable types"1374 else:1375 msg = "invalid key"1376 with pytest.raises(TypeError, match=msg):1377 index.get_loc([1, 2], method=method)1378 @pytest.mark.parametrize(1379 "method,loc", [("pad", 1), ("backfill", 2), ("nearest", 1)]1380 )1381 def test_get_loc_tolerance(self, method, loc):1382 index = pd.Index([0, 1, 2])1383 assert index.get_loc(1.1, method) == loc1384 assert index.get_loc(1.1, method, tolerance=1) == loc1385 @pytest.mark.parametrize("method", ["pad", "backfill", "nearest"])1386 def test_get_loc_outside_tolerance_raises(self, method):1387 index = pd.Index([0, 1, 2])1388 with pytest.raises(KeyError, match="1.1"):1389 index.get_loc(1.1, method, tolerance=0.05)1390 def test_get_loc_bad_tolerance_raises(self):1391 index = pd.Index([0, 1, 2])1392 with pytest.raises(ValueError, match="must be numeric"):1393 index.get_loc(1.1, "nearest", tolerance="invalid")1394 def test_get_loc_tolerance_no_method_raises(self):1395 index = pd.Index([0, 1, 2])1396 with pytest.raises(ValueError, match="tolerance .* valid if"):1397 index.get_loc(1.1, tolerance=1)1398 def test_get_loc_raises_missized_tolerance(self):1399 index = pd.Index([0, 1, 2])1400 with pytest.raises(ValueError, match="tolerance size must match"):1401 index.get_loc(1.1, "nearest", tolerance=[1, 1])1402 def test_get_loc_raises_object_nearest(self):1403 index = pd.Index(["a", "c"])1404 with pytest.raises(TypeError, match="unsupported operand type"):1405 index.get_loc("a", method="nearest")1406 def test_get_loc_raises_object_tolerance(self):1407 index = pd.Index(["a", "c"])1408 with pytest.raises(TypeError, match="unsupported operand type"):1409 index.get_loc("a", method="pad", tolerance="invalid")1410 @pytest.mark.parametrize("dtype", [int, float])1411 def test_slice_locs(self, dtype):1412 index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))1413 n = len(index)1414 assert index.slice_locs(start=2) == (2, n)1415 assert index.slice_locs(start=3) == (3, n)1416 assert index.slice_locs(3, 8) == (3, 6)1417 assert index.slice_locs(5, 10) == (3, n)1418 assert index.slice_locs(end=8) == (0, 6)1419 assert index.slice_locs(end=9) == (0, 7)1420 # reversed1421 index2 = index[::-1]1422 assert index2.slice_locs(8, 2) == (2, 6)1423 assert index2.slice_locs(7, 3) == (2, 5)1424 @pytest.mark.parametrize("dtype", [int, float])1425 def test_slice_float_locs(self, dtype):1426 index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))1427 n = len(index)1428 assert index.slice_locs(5.0, 10.0) == (3, n)1429 assert index.slice_locs(4.5, 10.5) == (3, 8)1430 index2 = index[::-1]1431 assert index2.slice_locs(8.5, 1.5) == (2, 6)1432 assert index2.slice_locs(10.5, -1) == (0, n)1433 def test_slice_locs_dup(self):1434 index = Index(["a", "a", "b", "c", "d", "d"])1435 assert index.slice_locs("a", "d") == (0, 6)1436 assert index.slice_locs(end="d") == (0, 6)1437 assert index.slice_locs("a", "c") == (0, 4)1438 assert index.slice_locs("b", "d") == (2, 6)1439 index2 = index[::-1]1440 assert index2.slice_locs("d", "a") == (0, 6)1441 assert index2.slice_locs(end="a") == (0, 6)1442 assert index2.slice_locs("d", "b") == (0, 4)1443 assert index2.slice_locs("c", "a") == (2, 6)1444 @pytest.mark.parametrize("dtype", [int, float])1445 def test_slice_locs_dup_numeric(self, dtype):1446 index = Index(np.array([10, 12, 12, 14], dtype=dtype))1447 assert index.slice_locs(12, 12) == (1, 3)1448 assert index.slice_locs(11, 13) == (1, 3)1449 index2 = index[::-1]1450 assert index2.slice_locs(12, 12) == (1, 3)1451 assert index2.slice_locs(13, 11) == (1, 3)1452 def test_slice_locs_na(self):1453 index = Index([np.nan, 1, 2])1454 assert index.slice_locs(1) == (1, 3)1455 assert index.slice_locs(np.nan) == (0, 3)1456 index = Index([0, np.nan, np.nan, 1, 2])1457 assert index.slice_locs(np.nan) == (1, 5)1458 def test_slice_locs_na_raises(self):1459 index = Index([np.nan, 1, 2])1460 with pytest.raises(KeyError, match=""):1461 index.slice_locs(start=1.5)1462 with pytest.raises(KeyError, match=""):1463 index.slice_locs(end=1.5)1464 @pytest.mark.parametrize(1465 "in_slice,expected",1466 [1467 (pd.IndexSlice[::-1], "yxdcb"),1468 (pd.IndexSlice["b":"y":-1], ""),1469 (pd.IndexSlice["b"::-1], "b"),1470 (pd.IndexSlice[:"b":-1], "yxdcb"),1471 (pd.IndexSlice[:"y":-1], "y"),1472 (pd.IndexSlice["y"::-1], "yxdcb"),1473 (pd.IndexSlice["y"::-4], "yb"),1474 # absent labels1475 (pd.IndexSlice[:"a":-1], "yxdcb"),1476 (pd.IndexSlice[:"a":-2], "ydb"),1477 (pd.IndexSlice["z"::-1], "yxdcb"),1478 (pd.IndexSlice["z"::-3], "yc"),1479 (pd.IndexSlice["m"::-1], "dcb"),1480 (pd.IndexSlice[:"m":-1], "yx"),1481 (pd.IndexSlice["a":"a":-1], ""),1482 (pd.IndexSlice["z":"z":-1], ""),1483 (pd.IndexSlice["m":"m":-1], ""),1484 ],1485 )1486 def test_slice_locs_negative_step(self, in_slice, expected):1487 index = Index(list("bcdxy"))1488 s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)1489 result = index[s_start : s_stop : in_slice.step]1490 expected = pd.Index(list(expected))1491 tm.assert_index_equal(result, expected)1492 def test_drop_by_str_label(self):1493 # TODO: Parametrize these after replacing self.strIndex with fixture1494 n = len(self.strIndex)1495 drop = self.strIndex[list(range(5, 10))]1496 dropped = self.strIndex.drop(drop)1497 expected = self.strIndex[list(range(5)) + list(range(10, n))]1498 tm.assert_index_equal(dropped, expected)1499 dropped = self.strIndex.drop(self.strIndex[0])1500 expected = self.strIndex[1:]1501 tm.assert_index_equal(dropped, expected)1502 @pytest.mark.parametrize("keys", [["foo", "bar"], ["1", "bar"]])1503 def test_drop_by_str_label_raises_missing_keys(self, keys):1504 with pytest.raises(KeyError, match=""):1505 self.strIndex.drop(keys)1506 def test_drop_by_str_label_errors_ignore(self):1507 # TODO: Parametrize these after replacing self.strIndex with fixture1508 # errors='ignore'1509 n = len(self.strIndex)1510 drop = self.strIndex[list(range(5, 10))]1511 mixed = drop.tolist() + ["foo"]1512 dropped = self.strIndex.drop(mixed, errors="ignore")1513 expected = self.strIndex[list(range(5)) + list(range(10, n))]1514 tm.assert_index_equal(dropped, expected)1515 dropped = self.strIndex.drop(["foo", "bar"], errors="ignore")1516 expected = self.strIndex[list(range(n))]1517 tm.assert_index_equal(dropped, expected)1518 def test_drop_by_numeric_label_loc(self):1519 # TODO: Parametrize numeric and str tests after self.strIndex fixture1520 index = Index([1, 2, 3])1521 dropped = index.drop(1)1522 expected = Index([2, 3])1523 tm.assert_index_equal(dropped, expected)1524 def test_drop_by_numeric_label_raises_missing_keys(self):1525 index = Index([1, 2, 3])1526 with pytest.raises(KeyError, match=""):1527 index.drop([3, 4])1528 @pytest.mark.parametrize(1529 "key,expected", [(4, Index([1, 2, 3])), ([3, 4, 5], Index([1, 2]))]1530 )1531 def test_drop_by_numeric_label_errors_ignore(self, key, expected):1532 index = Index([1, 2, 3])1533 dropped = index.drop(key, errors="ignore")1534 tm.assert_index_equal(dropped, expected)1535 @pytest.mark.parametrize(1536 "values",1537 [["a", "b", ("c", "d")], ["a", ("c", "d"), "b"], [("c", "d"), "a", "b"]],1538 )1539 @pytest.mark.parametrize("to_drop", [[("c", "d"), "a"], ["a", ("c", "d")]])1540 def test_drop_tuple(self, values, to_drop):1541 # GH 183041542 index = pd.Index(values)1543 expected = pd.Index(["b"])1544 result = index.drop(to_drop)1545 tm.assert_index_equal(result, expected)1546 removed = index.drop(to_drop[0])1547 for drop_me in to_drop[1], [to_drop[1]]:1548 result = removed.drop(drop_me)1549 tm.assert_index_equal(result, expected)1550 removed = index.drop(to_drop[1])1551 msg = r"\"\[{}\] not found in axis\"".format(re.escape(to_drop[1].__repr__()))1552 for drop_me in to_drop[1], [to_drop[1]]:1553 with pytest.raises(KeyError, match=msg):1554 removed.drop(drop_me)1555 @pytest.mark.parametrize(1556 "method,expected,sort",1557 [1558 (1559 "intersection",1560 np.array(1561 [(1, "A"), (2, "A"), (1, "B"), (2, "B")],1562 dtype=[("num", int), ("let", "a1")],1563 ),1564 False,1565 ),1566 (1567 "intersection",1568 np.array(1569 [(1, "A"), (1, "B"), (2, "A"), (2, "B")],1570 dtype=[("num", int), ("let", "a1")],1571 ),1572 None,1573 ),1574 (1575 "union",1576 np.array(1577 [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],1578 dtype=[("num", int), ("let", "a1")],1579 ),1580 None,1581 ),1582 ],1583 )1584 def test_tuple_union_bug(self, method, expected, sort):1585 index1 = Index(1586 np.array(1587 [(1, "A"), (2, "A"), (1, "B"), (2, "B")],1588 dtype=[("num", int), ("let", "a1")],1589 )1590 )1591 index2 = Index(1592 np.array(1593 [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],1594 dtype=[("num", int), ("let", "a1")],1595 )1596 )1597 result = getattr(index1, method)(index2, sort=sort)1598 assert result.ndim == 11599 expected = Index(expected)1600 tm.assert_index_equal(result, expected)1601 @pytest.mark.parametrize(1602 "attr",1603 [1604 "is_monotonic_increasing",1605 "is_monotonic_decreasing",1606 "_is_strictly_monotonic_increasing",1607 "_is_strictly_monotonic_decreasing",1608 ],1609 )1610 def test_is_monotonic_incomparable(self, attr):1611 index = Index([5, datetime.now(), 7])1612 assert not getattr(index, attr)1613 def test_get_set_value(self):1614 # TODO: Remove function? GH 197281615 values = np.random.randn(100)1616 date = self.dateIndex[67]1617 assert_almost_equal(self.dateIndex.get_value(values, date), values[67])1618 self.dateIndex.set_value(values, date, 10)1619 assert values[67] == 101620 @pytest.mark.parametrize("values", [["foo", "bar", "quux"], {"foo", "bar", "quux"}])1621 @pytest.mark.parametrize(1622 "index,expected",1623 [1624 (Index(["qux", "baz", "foo", "bar"]), np.array([False, False, True, True])),1625 (Index([]), np.array([], dtype=bool)), # empty1626 ],1627 )1628 def test_isin(self, values, index, expected):1629 result = index.isin(values)1630 tm.assert_numpy_array_equal(result, expected)1631 def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):1632 # Test cartesian product of null fixtures and ensure that we don't1633 # mangle the various types (save a corner case with PyPy)1634 # all nans are the same1635 if (1636 isinstance(nulls_fixture, float)1637 and isinstance(nulls_fixture2, float)1638 and math.isnan(nulls_fixture)1639 and math.isnan(nulls_fixture2)1640 ):1641 tm.assert_numpy_array_equal(1642 Index(["a", nulls_fixture]).isin([nulls_fixture2]),1643 np.array([False, True]),1644 )1645 elif nulls_fixture is nulls_fixture2: # should preserve NA type1646 tm.assert_numpy_array_equal(1647 Index(["a", nulls_fixture]).isin([nulls_fixture2]),1648 np.array([False, True]),1649 )1650 else:1651 tm.assert_numpy_array_equal(1652 Index(["a", nulls_fixture]).isin([nulls_fixture2]),1653 np.array([False, False]),1654 )1655 def test_isin_nan_common_float64(self, nulls_fixture):1656 if nulls_fixture is pd.NaT:1657 pytest.skip("pd.NaT not compatible with Float64Index")1658 # Float64Index overrides isin, so must be checked separately1659 tm.assert_numpy_array_equal(1660 Float64Index([1.0, nulls_fixture]).isin([np.nan]), np.array([False, True])1661 )1662 # we cannot compare NaT with NaN1663 tm.assert_numpy_array_equal(1664 Float64Index([1.0, nulls_fixture]).isin([pd.NaT]), np.array([False, False])1665 )1666 @pytest.mark.parametrize("level", [0, -1])1667 @pytest.mark.parametrize(1668 "index",1669 [1670 Index(["qux", "baz", "foo", "bar"]),1671 # Float64Index overrides isin, so must be checked separately1672 Float64Index([1.0, 2.0, 3.0, 4.0]),1673 ],1674 )1675 def test_isin_level_kwarg(self, level, index):1676 values = index.tolist()[-2:] + ["nonexisting"]1677 expected = np.array([False, False, True, True])1678 tm.assert_numpy_array_equal(expected, index.isin(values, level=level))1679 index.name = "foobar"1680 tm.assert_numpy_array_equal(expected, index.isin(values, level="foobar"))1681 @pytest.mark.parametrize("level", [2, 10, -3])1682 def test_isin_level_kwarg_bad_level_raises(self, level, indices):1683 index = indices1684 with pytest.raises(IndexError, match="Too many levels"):1685 index.isin([], level=level)1686 @pytest.mark.parametrize("label", [1.0, "foobar", "xyzzy", np.nan])1687 def test_isin_level_kwarg_bad_label_raises(self, label, indices):1688 index = indices1689 if isinstance(index, MultiIndex):1690 index = index.rename(["foo", "bar"])1691 msg = "'Level {} not found'"1692 else:1693 index = index.rename("foo")1694 msg = r"'Level {} must be same as name \(foo\)'"1695 with pytest.raises(KeyError, match=msg.format(label)):1696 index.isin([], level=label)1697 @pytest.mark.parametrize("empty", [[], Series(), np.array([])])1698 def test_isin_empty(self, empty):1699 # see gh-169911700 index = Index(["a", "b"])1701 expected = np.array([False, False])1702 result = index.isin(empty)1703 tm.assert_numpy_array_equal(expected, result)1704 @pytest.mark.parametrize(1705 "values",1706 [1707 [1, 2, 3, 4],1708 [1.0, 2.0, 3.0, 4.0],1709 [True, True, True, True],1710 ["foo", "bar", "baz", "qux"],1711 pd.date_range("2018-01-01", freq="D", periods=4),1712 ],1713 )1714 def test_boolean_cmp(self, values):1715 index = Index(values)1716 result = index == values1717 expected = np.array([True, True, True, True], dtype=bool)1718 tm.assert_numpy_array_equal(result, expected)1719 @pytest.mark.parametrize("name,level", [(None, 0), ("a", "a")])1720 def test_get_level_values(self, name, level):1721 expected = self.strIndex.copy()1722 if name:1723 expected.name = name1724 result = expected.get_level_values(level)1725 tm.assert_index_equal(result, expected)1726 def test_slice_keep_name(self):1727 index = Index(["a", "b"], name="asdf")1728 assert index.name == index[1:].name1729 # instance attributes of the form self.<name>Index1730 @pytest.mark.parametrize("index_kind", ["unicode", "str", "date", "int", "float"])1731 def test_join_self(self, join_type, index_kind):1732 res = getattr(self, "{0}Index".format(index_kind))1733 joined = res.join(res, how=join_type)1734 assert res is joined1735 @pytest.mark.parametrize("method", ["strip", "rstrip", "lstrip"])1736 def test_str_attribute(self, method):1737 # GH90681738 index = Index([" jack", "jill ", " jesse ", "frank"])1739 expected = Index([getattr(str, method)(x) for x in index.values])1740 result = getattr(index.str, method)()1741 tm.assert_index_equal(result, expected)1742 @pytest.mark.parametrize(1743 "index",1744 [1745 Index(range(5)),1746 tm.makeDateIndex(10),1747 MultiIndex.from_tuples([("foo", "1"), ("bar", "3")]),1748 period_range(start="2000", end="2010", freq="A"),1749 ],1750 )1751 def test_str_attribute_raises(self, index):1752 with pytest.raises(AttributeError, match="only use .str accessor"):1753 index.str.repeat(2)1754 @pytest.mark.parametrize(1755 "expand,expected",1756 [1757 (None, Index([["a", "b", "c"], ["d", "e"], ["f"]])),1758 (False, Index([["a", "b", "c"], ["d", "e"], ["f"]])),1759 (1760 True,1761 MultiIndex.from_tuples(1762 [("a", "b", "c"), ("d", "e", np.nan), ("f", np.nan, np.nan)]1763 ),1764 ),1765 ],1766 )1767 def test_str_split(self, expand, expected):1768 index = Index(["a b c", "d e", "f"])1769 if expand is not None:1770 result = index.str.split(expand=expand)1771 else:1772 result = index.str.split()1773 tm.assert_index_equal(result, expected)1774 def test_str_bool_return(self):1775 # test boolean case, should return np.array instead of boolean Index1776 index = Index(["a1", "a2", "b1", "b2"])1777 result = index.str.startswith("a")1778 expected = np.array([True, True, False, False])1779 tm.assert_numpy_array_equal(result, expected)1780 assert isinstance(result, np.ndarray)1781 def test_str_bool_series_indexing(self):1782 index = Index(["a1", "a2", "b1", "b2"])1783 s = Series(range(4), index=index)1784 result = s[s.index.str.startswith("a")]1785 expected = Series(range(2), index=["a1", "a2"])1786 tm.assert_series_equal(result, expected)1787 @pytest.mark.parametrize(1788 "index,expected", [(Index(list("abcd")), True), (Index(range(4)), False)]1789 )1790 def test_tab_completion(self, index, expected):1791 # GH 99101792 result = "str" in dir(index)1793 assert result == expected1794 def test_indexing_doesnt_change_class(self):1795 index = Index([1, 2, 3, "a", "b", "c"])1796 assert index[1:3].identical(pd.Index([2, 3], dtype=np.object_))1797 assert index[[0, 1]].identical(pd.Index([1, 2], dtype=np.object_))1798 def test_outer_join_sort(self):1799 left_index = Index(np.random.permutation(15))1800 right_index = tm.makeDateIndex(10)1801 with tm.assert_produces_warning(RuntimeWarning):1802 result = left_index.join(right_index, how="outer")1803 # right_index in this case because DatetimeIndex has join precedence1804 # over Int64Index1805 with tm.assert_produces_warning(RuntimeWarning):1806 expected = right_index.astype(object).union(left_index.astype(object))1807 tm.assert_index_equal(result, expected)1808 def test_nan_first_take_datetime(self):1809 index = Index([pd.NaT, Timestamp("20130101"), Timestamp("20130102")])1810 result = index.take([-1, 0, 1])1811 expected = Index([index[-1], index[0], index[1]])1812 tm.assert_index_equal(result, expected)1813 def test_take_fill_value(self):1814 # GH 126311815 index = pd.Index(list("ABC"), name="xxx")1816 result = index.take(np.array([1, 0, -1]))1817 expected = pd.Index(list("BAC"), name="xxx")1818 tm.assert_index_equal(result, expected)1819 # fill_value1820 result = index.take(np.array([1, 0, -1]), fill_value=True)1821 expected = pd.Index(["B", "A", np.nan], name="xxx")1822 tm.assert_index_equal(result, expected)1823 # allow_fill=False1824 result = index.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)1825 expected = pd.Index(["B", "A", "C"], name="xxx")1826 tm.assert_index_equal(result, expected)1827 def test_take_fill_value_none_raises(self):1828 index = pd.Index(list("ABC"), name="xxx")1829 msg = (1830 "When allow_fill=True and fill_value is not None, "1831 "all indices must be >= -1"1832 )1833 with pytest.raises(ValueError, match=msg):1834 index.take(np.array([1, 0, -2]), fill_value=True)1835 with pytest.raises(ValueError, match=msg):1836 index.take(np.array([1, 0, -5]), fill_value=True)1837 def test_take_bad_bounds_raises(self):1838 index = pd.Index(list("ABC"), name="xxx")1839 with pytest.raises(IndexError, match="out of bounds"):1840 index.take(np.array([1, -5]))1841 @pytest.mark.parametrize("name", [None, "foobar"])1842 @pytest.mark.parametrize(1843 "labels",1844 [1845 [],1846 np.array([]),1847 ["A", "B", "C"],1848 ["C", "B", "A"],1849 np.array(["A", "B", "C"]),1850 np.array(["C", "B", "A"]),1851 # Must preserve name even if dtype changes1852 pd.date_range("20130101", periods=3).values,1853 pd.date_range("20130101", periods=3).tolist(),1854 ],1855 )1856 def test_reindex_preserves_name_if_target_is_list_or_ndarray(self, name, labels):1857 # GH65521858 index = pd.Index([0, 1, 2])1859 index.name = name1860 assert index.reindex(labels)[0].name == name1861 @pytest.mark.parametrize("labels", [[], np.array([]), np.array([], dtype=np.int64)])1862 def test_reindex_preserves_type_if_target_is_empty_list_or_array(self, labels):1863 # GH77741864 index = pd.Index(list("abc"))1865 assert index.reindex(labels)[0].dtype.type == np.object_1866 @pytest.mark.parametrize(1867 "labels,dtype",1868 [1869 (pd.Int64Index([]), np.int64),1870 (pd.Float64Index([]), np.float64),1871 (pd.DatetimeIndex([]), np.datetime64),1872 ],1873 )1874 def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self, labels, dtype):1875 # GH77741876 index = pd.Index(list("abc"))1877 assert index.reindex(labels)[0].dtype.type == dtype1878 def test_reindex_no_type_preserve_target_empty_mi(self):1879 index = pd.Index(list("abc"))1880 result = index.reindex(1881 pd.MultiIndex([pd.Int64Index([]), pd.Float64Index([])], [[], []])1882 )[0]1883 assert result.levels[0].dtype.type == np.int641884 assert result.levels[1].dtype.type == np.float641885 def test_groupby(self):1886 index = Index(range(5))1887 result = index.groupby(np.array([1, 1, 2, 2, 2]))1888 expected = {1: pd.Index([0, 1]), 2: pd.Index([2, 3, 4])}1889 tm.assert_dict_equal(result, expected)1890 @pytest.mark.parametrize(1891 "mi,expected",1892 [1893 (MultiIndex.from_tuples([(1, 2), (4, 5)]), np.array([True, True])),1894 (MultiIndex.from_tuples([(1, 2), (4, 6)]), np.array([True, False])),1895 ],1896 )1897 def test_equals_op_multiindex(self, mi, expected):1898 # GH97851899 # test comparisons of multiindex1900 df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])1901 result = df.index == mi1902 tm.assert_numpy_array_equal(result, expected)1903 def test_equals_op_multiindex_identify(self):1904 df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])1905 result = df.index == df.index1906 expected = np.array([True, True])1907 tm.assert_numpy_array_equal(result, expected)1908 @pytest.mark.parametrize(1909 "index",1910 [1911 MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)]),1912 Index(["foo", "bar", "baz"]),1913 ],1914 )1915 def test_equals_op_mismatched_multiindex_raises(self, index):1916 df = pd.read_csv(StringIO("a,b,c\n1,2,3\n4,5,6"), index_col=[0, 1])1917 with pytest.raises(ValueError, match="Lengths must match"):1918 df.index == index1919 def test_equals_op_index_vs_mi_same_length(self):1920 mi = MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)])1921 index = Index(["foo", "bar", "baz"])1922 result = mi == index1923 expected = np.array([False, False, False])1924 tm.assert_numpy_array_equal(result, expected)1925 @pytest.mark.parametrize("dt_conv", [pd.to_datetime, pd.to_timedelta])1926 def test_dt_conversion_preserves_name(self, dt_conv):1927 # GH 108751928 index = pd.Index(["01:02:03", "01:02:04"], name="label")1929 assert index.name == dt_conv(index).name1930 @pytest.mark.parametrize(1931 "index,expected",1932 [1933 # ASCII1934 # short1935 (1936 pd.Index(["a", "bb", "ccc"]),1937 """Index(['a', 'bb', 'ccc'], dtype='object')""",1938 ),1939 # multiple lines1940 (1941 pd.Index(["a", "bb", "ccc"] * 10),1942 """\1943Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',1944 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc',1945 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],1946 dtype='object')""",1947 ),1948 # truncated1949 (1950 pd.Index(["a", "bb", "ccc"] * 100),1951 """\1952Index(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',1953 ...1954 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],1955 dtype='object', length=300)""",1956 ),1957 # Non-ASCII1958 # short1959 (1960 pd.Index(["あ", "いい", "ううう"]),1961 """Index(['あ', 'いい', 'ううう'], dtype='object')""",1962 ),1963 # multiple lines1964 (1965 pd.Index(["あ", "いい", "ううう"] * 10),1966 (1967 "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "1968 "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"1969 " 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "1970 "'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',\n"1971 " 'あ', 'いい', 'ううう', 'あ', 'いい', "1972 "'ううう'],\n"1973 " dtype='object')"1974 ),1975 ),1976 # truncated1977 (1978 pd.Index(["あ", "いい", "ううう"] * 100),1979 (1980 "Index(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', "1981 "'あ', 'いい', 'ううう', 'あ',\n"1982 " ...\n"1983 " 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', "1984 "'ううう', 'あ', 'いい', 'ううう'],\n"1985 " dtype='object', length=300)"1986 ),1987 ),1988 ],1989 )1990 def test_string_index_repr(self, index, expected):1991 result = repr(index)1992 assert result == expected1993 @pytest.mark.parametrize(1994 "index,expected",1995 [1996 # short1997 (1998 pd.Index(["あ", "いい", "ううう"]),1999 ("Index(['あ', 'いい', 'ううう'], dtype='object')"),2000 ),2001 # multiple lines2002 (2003 pd.Index(["あ", "いい", "ううう"] * 10),2004 (2005 "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "2006 "'ううう', 'あ', 'いい', 'ううう',\n"2007 " 'あ', 'いい', 'ううう', 'あ', 'いい', "2008 "'ううう', 'あ', 'いい', 'ううう',\n"2009 " 'あ', 'いい', 'ううう', 'あ', 'いい', "2010 "'ううう', 'あ', 'いい', 'ううう',\n"2011 " 'あ', 'いい', 'ううう'],\n"2012 " dtype='object')"2013 ""2014 ),2015 ),2016 # truncated2017 (2018 pd.Index(["あ", "いい", "ううう"] * 100),2019 (2020 "Index(['あ', 'いい', 'ううう', 'あ', 'いい', "2021 "'ううう', 'あ', 'いい', 'ううう',\n"2022 " 'あ',\n"2023 " ...\n"2024 " 'ううう', 'あ', 'いい', 'ううう', 'あ', "2025 "'いい', 'ううう', 'あ', 'いい',\n"2026 " 'ううう'],\n"2027 " dtype='object', length=300)"2028 ),2029 ),2030 ],2031 )2032 def test_string_index_repr_with_unicode_option(self, index, expected):2033 # Enable Unicode option -----------------------------------------2034 with cf.option_context("display.unicode.east_asian_width", True):2035 result = repr(index)2036 assert result == expected2037 def test_cached_properties_not_settable(self):2038 index = pd.Index([1, 2, 3])2039 with pytest.raises(AttributeError, match="Can't set attribute"):2040 index.is_unique = False2041 def test_get_duplicates_deprecated(self):2042 index = pd.Index([1, 2, 3])2043 with tm.assert_produces_warning(FutureWarning):2044 index.get_duplicates()2045 def test_tab_complete_warning(self, ip):2046 # https://github.com/pandas-dev/pandas/issues/164092047 pytest.importorskip("IPython", minversion="6.0.0")2048 from IPython.core.completer import provisionalcompleter2049 code = "import pandas as pd; idx = pd.Index([1, 2])"2050 ip.run_code(code)2051 with tm.assert_produces_warning(None):2052 with provisionalcompleter("ignore"):2053 list(ip.Completer.completions("idx.", 4))2054 def test_deprecated_contains(self):2055 for index in self.indices.values():2056 with tm.assert_produces_warning(FutureWarning):2057 index.contains(1)2058class TestMixedIntIndex(Base):2059 # Mostly the tests from common.py for which the results differ2060 # in py2 and py3 because ints and strings are uncomparable in py32061 # (GH 13514)2062 _holder = Index2063 def setup_method(self, method):2064 self.indices = dict(mixedIndex=Index([0, "a", 1, "b", 2, "c"]))2065 self.setup_indices()2066 def create_index(self):2067 return self.mixedIndex2068 def test_argsort(self):2069 index = self.create_index()2070 if PY36:2071 with pytest.raises(TypeError, match="'>|<' not supported"):2072 index.argsort()2073 else:2074 with pytest.raises(TypeError, match="unorderable types"):2075 index.argsort()2076 def test_numpy_argsort(self):2077 index = self.create_index()2078 if PY36:2079 with pytest.raises(TypeError, match="'>|<' not supported"):2080 np.argsort(index)2081 else:2082 with pytest.raises(TypeError, match="unorderable types"):2083 np.argsort(index)2084 def test_copy_name(self):2085 # Check that "name" argument passed at initialization is honoured2086 # GH123092087 index = self.create_index()2088 first = index.__class__(index, copy=True, name="mario")2089 second = first.__class__(first, copy=False)2090 # Even though "copy=False", we want a new object.2091 assert first is not second2092 tm.assert_index_equal(first, second)2093 assert first.name == "mario"2094 assert second.name == "mario"2095 s1 = Series(2, index=first)2096 s2 = Series(3, index=second[:-1])2097 s3 = s1 * s22098 assert s3.index.name == "mario"2099 def test_copy_name2(self):2100 # Check that adding a "name" parameter to the copy is honored2101 # GH143022102 index = pd.Index([1, 2], name="MyName")2103 index1 = index.copy()2104 tm.assert_index_equal(index, index1)2105 index2 = index.copy(name="NewName")2106 tm.assert_index_equal(index, index2, check_names=False)2107 assert index.name == "MyName"2108 assert index2.name == "NewName"2109 index3 = index.copy(names=["NewName"])2110 tm.assert_index_equal(index, index3, check_names=False)2111 assert index.name == "MyName"2112 assert index.names == ["MyName"]2113 assert index3.name == "NewName"2114 assert index3.names == ["NewName"]2115 def test_union_base(self):2116 index = self.create_index()2117 first = index[3:]2118 second = index[:5]2119 result = first.union(second)2120 expected = Index([0, 1, 2, "a", "b", "c"])2121 tm.assert_index_equal(result, expected)2122 @pytest.mark.parametrize("klass", [np.array, Series, list])2123 def test_union_different_type_base(self, klass):2124 # GH 101492125 index = self.create_index()2126 first = index[3:]2127 second = index[:5]2128 result = first.union(klass(second.values))2129 assert tm.equalContents(result, index)2130 def test_unique_na(self):2131 idx = pd.Index([2, np.nan, 2, 1], name="my_index")2132 expected = pd.Index([2, np.nan, 1], name="my_index")2133 result = idx.unique()2134 tm.assert_index_equal(result, expected)2135 @pytest.mark.parametrize("sort", [None, False])2136 def test_intersection_base(self, sort):2137 # (same results for py2 and py3 but sortedness not tested elsewhere)2138 index = self.create_index()2139 first = index[:5]2140 second = index[:3]2141 expected = Index([0, 1, "a"]) if sort is None else Index([0, "a", 1])2142 result = first.intersection(second, sort=sort)2143 tm.assert_index_equal(result, expected)2144 @pytest.mark.parametrize("klass", [np.array, Series, list])2145 @pytest.mark.parametrize("sort", [None, False])2146 def test_intersection_different_type_base(self, klass, sort):2147 # GH 101492148 index = self.create_index()2149 first = index[:5]2150 second = index[:3]2151 result = first.intersection(klass(second.values), sort=sort)2152 assert tm.equalContents(result, second)2153 @pytest.mark.parametrize("sort", [None, False])2154 def test_difference_base(self, sort):2155 # (same results for py2 and py3 but sortedness not tested elsewhere)2156 index = self.create_index()2157 first = index[:4]2158 second = index[3:]2159 result = first.difference(second, sort)2160 expected = Index([0, "a", 1])2161 if sort is None:2162 expected = Index(safe_sort(expected))2163 tm.assert_index_equal(result, expected)2164 def test_symmetric_difference(self):2165 # (same results for py2 and py3 but sortedness not tested elsewhere)2166 index = self.create_index()2167 first = index[:4]2168 second = index[3:]2169 result = first.symmetric_difference(second)2170 expected = Index([0, 1, 2, "a", "c"])2171 tm.assert_index_equal(result, expected)2172 def test_logical_compat(self):2173 index = self.create_index()2174 assert index.all() == index.values.all()2175 assert index.any() == index.values.any()2176 @pytest.mark.parametrize("how", ["any", "all"])2177 @pytest.mark.parametrize("dtype", [None, object, "category"])2178 @pytest.mark.parametrize(2179 "vals,expected",2180 [2181 ([1, 2, 3], [1, 2, 3]),2182 ([1.0, 2.0, 3.0], [1.0, 2.0, 3.0]),2183 ([1.0, 2.0, np.nan, 3.0], [1.0, 2.0, 3.0]),2184 (["A", "B", "C"], ["A", "B", "C"]),2185 (["A", np.nan, "B", "C"], ["A", "B", "C"]),2186 ],2187 )2188 def test_dropna(self, how, dtype, vals, expected):2189 # GH 61942190 index = pd.Index(vals, dtype=dtype)2191 result = index.dropna(how=how)2192 expected = pd.Index(expected, dtype=dtype)2193 tm.assert_index_equal(result, expected)2194 @pytest.mark.parametrize("how", ["any", "all"])2195 @pytest.mark.parametrize(2196 "index,expected",2197 [2198 (2199 pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),2200 pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),2201 ),2202 (2203 pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", pd.NaT]),2204 pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),2205 ),2206 (2207 pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),2208 pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),2209 ),2210 (2211 pd.TimedeltaIndex([pd.NaT, "1 days", "2 days", "3 days", pd.NaT]),2212 pd.TimedeltaIndex(["1 days", "2 days", "3 days"]),2213 ),2214 (2215 pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),2216 pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),2217 ),2218 (2219 pd.PeriodIndex(["2012-02", "2012-04", "NaT", "2012-05"], freq="M"),2220 pd.PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),2221 ),2222 ],2223 )2224 def test_dropna_dt_like(self, how, index, expected):2225 result = index.dropna(how=how)2226 tm.assert_index_equal(result, expected)2227 def test_dropna_invalid_how_raises(self):2228 msg = "invalid how option: xxx"2229 with pytest.raises(ValueError, match=msg):2230 pd.Index([1, 2, 3]).dropna(how="xxx")2231 def test_get_combined_index(self):2232 result = _get_combined_index([])2233 expected = Index([])2234 tm.assert_index_equal(result, expected)2235 def test_repeat(self):2236 repeats = 22237 index = pd.Index([1, 2, 3])2238 expected = pd.Index([1, 1, 2, 2, 3, 3])2239 result = index.repeat(repeats)2240 tm.assert_index_equal(result, expected)2241 @pytest.mark.parametrize(2242 "index",2243 [2244 pd.Index([np.nan]),2245 pd.Index([np.nan, 1]),2246 pd.Index([1, 2, np.nan]),2247 pd.Index(["a", "b", np.nan]),2248 pd.to_datetime(["NaT"]),2249 pd.to_datetime(["NaT", "2000-01-01"]),2250 pd.to_datetime(["2000-01-01", "NaT", "2000-01-02"]),2251 pd.to_timedelta(["1 day", "NaT"]),2252 ],2253 )2254 def test_is_monotonic_na(self, index):2255 assert index.is_monotonic_increasing is False2256 assert index.is_monotonic_decreasing is False2257 assert index._is_strictly_monotonic_increasing is False2258 assert index._is_strictly_monotonic_decreasing is False2259 def test_repr_summary(self):2260 with cf.option_context("display.max_seq_items", 10):2261 result = repr(pd.Index(np.arange(1000)))2262 assert len(result) < 2002263 assert "..." in result2264 @pytest.mark.parametrize("klass", [Series, DataFrame])2265 def test_int_name_format(self, klass):2266 index = Index(["a", "b", "c"], name=0)2267 result = klass(list(range(3)), index=index)2268 assert "0" in repr(result)2269 def test_print_unicode_columns(self):2270 df = pd.DataFrame({"\u05d0": [1, 2, 3], "\u05d1": [4, 5, 6], "c": [7, 8, 9]})2271 repr(df.columns) # should not raise UnicodeDecodeError2272 def test_str_to_bytes_raises(self):2273 # GH 264472274 index = Index([str(x) for x in range(10)])2275 msg = "^'str' object cannot be interpreted as an integer$"2276 with pytest.raises(TypeError, match=msg):2277 bytes(index)2278 def test_intersect_str_dates(self):2279 dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]2280 index1 = Index(dt_dates, dtype=object)2281 index2 = Index(["aa"], dtype=object)2282 result = index2.intersection(index1)2283 expected = Index([], dtype=object)2284 tm.assert_index_equal(result, expected)2285class TestIndexUtils:2286 @pytest.mark.parametrize(2287 "data, names, expected",2288 [2289 ([[1, 2, 3]], None, Index([1, 2, 3])),2290 ([[1, 2, 3]], ["name"], Index([1, 2, 3], name="name")),2291 (2292 [["a", "a"], ["c", "d"]],2293 None,2294 MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]]),2295 ),2296 (2297 [["a", "a"], ["c", "d"]],2298 ["L1", "L2"],2299 MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]], names=["L1", "L2"]),2300 ),2301 ],2302 )2303 def test_ensure_index_from_sequences(self, data, names, expected):2304 result = ensure_index_from_sequences(data, names)2305 tm.assert_index_equal(result, expected)2306 def test_ensure_index_mixed_closed_intervals(self):2307 # GH271722308 intervals = [2309 pd.Interval(0, 1, closed="left"),2310 pd.Interval(1, 2, closed="right"),2311 pd.Interval(2, 3, closed="neither"),2312 pd.Interval(3, 4, closed="both"),2313 ]2314 result = ensure_index(intervals)2315 expected = Index(intervals, dtype=object)2316 tm.assert_index_equal(result, expected)2317@pytest.mark.parametrize(2318 "opname",2319 [2320 "eq",2321 "ne",2322 "le",2323 "lt",2324 "ge",2325 "gt",2326 "add",2327 "radd",2328 "sub",...

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test_alter_axes.py

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...30 df.index = idx31 tm.assert_index_equal(df.index, idx)32 with pytest.raises(ValueError, match="Length mismatch"):33 df.index = idx[::2]34 def test_set_index(self, float_string_frame):35 df = float_string_frame36 idx = Index(np.arange(len(df))[::-1])37 df = df.set_index(idx)38 tm.assert_index_equal(df.index, idx)39 with pytest.raises(ValueError, match="Length mismatch"):40 df.set_index(idx[::2])41 def test_set_index_cast(self):42 # issue casting an index then set_index43 df = DataFrame(44 {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2]}, index=[2010, 2011, 2012]45 )46 df2 = df.set_index(df.index.astype(np.int32))47 tm.assert_frame_equal(df, df2)48 # A has duplicate values, C does not49 @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])50 @pytest.mark.parametrize("inplace", [True, False])51 @pytest.mark.parametrize("drop", [True, False])52 def test_set_index_drop_inplace(self, frame_of_index_cols, drop, inplace, keys):53 df = frame_of_index_cols54 if isinstance(keys, list):55 idx = MultiIndex.from_arrays([df[x] for x in keys], names=keys)56 else:57 idx = Index(df[keys], name=keys)58 expected = df.drop(keys, axis=1) if drop else df59 expected.index = idx60 if inplace:61 result = df.copy()62 result.set_index(keys, drop=drop, inplace=True)63 else:64 result = df.set_index(keys, drop=drop)65 tm.assert_frame_equal(result, expected)66 # A has duplicate values, C does not67 @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])68 @pytest.mark.parametrize("drop", [True, False])69 def test_set_index_append(self, frame_of_index_cols, drop, keys):70 df = frame_of_index_cols71 keys = keys if isinstance(keys, list) else [keys]72 idx = MultiIndex.from_arrays(73 [df.index] + [df[x] for x in keys], names=[None] + keys74 )75 expected = df.drop(keys, axis=1) if drop else df.copy()76 expected.index = idx77 result = df.set_index(keys, drop=drop, append=True)78 tm.assert_frame_equal(result, expected)79 # A has duplicate values, C does not80 @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")])81 @pytest.mark.parametrize("drop", [True, False])82 def test_set_index_append_to_multiindex(self, frame_of_index_cols, drop, keys):83 # append to existing multiindex84 df = frame_of_index_cols.set_index(["D"], drop=drop, append=True)85 keys = keys if isinstance(keys, list) else [keys]86 expected = frame_of_index_cols.set_index(["D"] + keys, drop=drop, append=True)87 result = df.set_index(keys, drop=drop, append=True)88 tm.assert_frame_equal(result, expected)89 def test_set_index_after_mutation(self):90 # GH159091 df = DataFrame({"val": [0, 1, 2], "key": ["a", "b", "c"]})92 expected = DataFrame({"val": [1, 2]}, Index(["b", "c"], name="key"))93 df2 = df.loc[df.index.map(lambda indx: indx >= 1)]94 result = df2.set_index("key")95 tm.assert_frame_equal(result, expected)96 # MultiIndex constructor does not work directly on Series -> lambda97 # Add list-of-list constructor because list is ambiguous -> lambda98 # also test index name if append=True (name is duplicate here for B)99 @pytest.mark.parametrize(100 "box",101 [102 Series,103 Index,104 np.array,105 list,106 lambda x: [list(x)],107 lambda x: MultiIndex.from_arrays([x]),108 ],109 )110 @pytest.mark.parametrize(111 "append, index_name", [(True, None), (True, "B"), (True, "test"), (False, None)]112 )113 @pytest.mark.parametrize("drop", [True, False])114 def test_set_index_pass_single_array(115 self, frame_of_index_cols, drop, append, index_name, box116 ):117 df = frame_of_index_cols118 df.index.name = index_name119 key = box(df["B"])120 if box == list:121 # list of strings gets interpreted as list of keys122 msg = "['one', 'two', 'three', 'one', 'two']"123 with pytest.raises(KeyError, match=msg):124 df.set_index(key, drop=drop, append=append)125 else:126 # np.array/list-of-list "forget" the name of B127 name_mi = getattr(key, "names", None)128 name = [getattr(key, "name", None)] if name_mi is None else name_mi129 result = df.set_index(key, drop=drop, append=append)130 # only valid column keys are dropped131 # since B is always passed as array above, nothing is dropped132 expected = df.set_index(["B"], drop=False, append=append)133 expected.index.names = [index_name] + name if append else name134 tm.assert_frame_equal(result, expected)135 # MultiIndex constructor does not work directly on Series -> lambda136 # also test index name if append=True (name is duplicate here for A & B)137 @pytest.mark.parametrize(138 "box", [Series, Index, np.array, list, lambda x: MultiIndex.from_arrays([x])]139 )140 @pytest.mark.parametrize(141 "append, index_name",142 [(True, None), (True, "A"), (True, "B"), (True, "test"), (False, None)],143 )144 @pytest.mark.parametrize("drop", [True, False])145 def test_set_index_pass_arrays(146 self, frame_of_index_cols, drop, append, index_name, box147 ):148 df = frame_of_index_cols149 df.index.name = index_name150 keys = ["A", box(df["B"])]151 # np.array/list "forget" the name of B152 names = ["A", None if box in [np.array, list, tuple, iter] else "B"]153 result = df.set_index(keys, drop=drop, append=append)154 # only valid column keys are dropped155 # since B is always passed as array above, only A is dropped, if at all156 expected = df.set_index(["A", "B"], drop=False, append=append)157 expected = expected.drop("A", axis=1) if drop else expected158 expected.index.names = [index_name] + names if append else names159 tm.assert_frame_equal(result, expected)160 # MultiIndex constructor does not work directly on Series -> lambda161 # We also emulate a "constructor" for the label -> lambda162 # also test index name if append=True (name is duplicate here for A)163 @pytest.mark.parametrize(164 "box2",165 [166 Series,167 Index,168 np.array,169 list,170 iter,171 lambda x: MultiIndex.from_arrays([x]),172 lambda x: x.name,173 ],174 )175 @pytest.mark.parametrize(176 "box1",177 [178 Series,179 Index,180 np.array,181 list,182 iter,183 lambda x: MultiIndex.from_arrays([x]),184 lambda x: x.name,185 ],186 )187 @pytest.mark.parametrize(188 "append, index_name", [(True, None), (True, "A"), (True, "test"), (False, None)]189 )190 @pytest.mark.parametrize("drop", [True, False])191 def test_set_index_pass_arrays_duplicate(192 self, frame_of_index_cols, drop, append, index_name, box1, box2193 ):194 df = frame_of_index_cols195 df.index.name = index_name196 keys = [box1(df["A"]), box2(df["A"])]197 result = df.set_index(keys, drop=drop, append=append)198 # if either box is iter, it has been consumed; re-read199 keys = [box1(df["A"]), box2(df["A"])]200 # need to adapt first drop for case that both keys are 'A' --201 # cannot drop the same column twice;202 # use "is" because == would give ambiguous Boolean error for containers203 first_drop = (204 False if (keys[0] is "A" and keys[1] is "A") else drop # noqa: F632205 )206 # to test against already-tested behaviour, we add sequentially,207 # hence second append always True; must wrap keys in list, otherwise208 # box = list would be interpreted as keys209 expected = df.set_index([keys[0]], drop=first_drop, append=append)210 expected = expected.set_index([keys[1]], drop=drop, append=True)211 tm.assert_frame_equal(result, expected)212 @pytest.mark.parametrize("append", [True, False])213 @pytest.mark.parametrize("drop", [True, False])214 def test_set_index_pass_multiindex(self, frame_of_index_cols, drop, append):215 df = frame_of_index_cols216 keys = MultiIndex.from_arrays([df["A"], df["B"]], names=["A", "B"])217 result = df.set_index(keys, drop=drop, append=append)218 # setting with a MultiIndex will never drop columns219 expected = df.set_index(["A", "B"], drop=False, append=append)220 tm.assert_frame_equal(result, expected)221 def test_set_index_verify_integrity(self, frame_of_index_cols):222 df = frame_of_index_cols223 with pytest.raises(ValueError, match="Index has duplicate keys"):224 df.set_index("A", verify_integrity=True)225 # with MultiIndex226 with pytest.raises(ValueError, match="Index has duplicate keys"):227 df.set_index([df["A"], df["A"]], verify_integrity=True)228 @pytest.mark.parametrize("append", [True, False])229 @pytest.mark.parametrize("drop", [True, False])230 def test_set_index_raise_keys(self, frame_of_index_cols, drop, append):231 df = frame_of_index_cols232 with pytest.raises(KeyError, match="['foo', 'bar', 'baz']"):233 # column names are A-E, as well as one tuple234 df.set_index(["foo", "bar", "baz"], drop=drop, append=append)235 # non-existent key in list with arrays236 with pytest.raises(KeyError, match="X"):237 df.set_index([df["A"], df["B"], "X"], drop=drop, append=append)238 msg = "[('foo', 'foo', 'foo', 'bar', 'bar')]"239 # tuples always raise KeyError240 with pytest.raises(KeyError, match=msg):241 df.set_index(tuple(df["A"]), drop=drop, append=append)242 # also within a list243 with pytest.raises(KeyError, match=msg):244 df.set_index(["A", df["A"], tuple(df["A"])], drop=drop, append=append)245 @pytest.mark.parametrize("append", [True, False])246 @pytest.mark.parametrize("drop", [True, False])247 @pytest.mark.parametrize("box", [set], ids=["set"])248 def test_set_index_raise_on_type(self, frame_of_index_cols, box, drop, append):249 df = frame_of_index_cols250 msg = 'The parameter "keys" may be a column key, .*'251 # forbidden type, e.g. set252 with pytest.raises(TypeError, match=msg):253 df.set_index(box(df["A"]), drop=drop, append=append)254 # forbidden type in list, e.g. set255 with pytest.raises(TypeError, match=msg):256 df.set_index(["A", df["A"], box(df["A"])], drop=drop, append=append)257 # MultiIndex constructor does not work directly on Series -> lambda258 @pytest.mark.parametrize(259 "box",260 [Series, Index, np.array, iter, lambda x: MultiIndex.from_arrays([x])],261 ids=["Series", "Index", "np.array", "iter", "MultiIndex"],262 )263 @pytest.mark.parametrize("length", [4, 6], ids=["too_short", "too_long"])264 @pytest.mark.parametrize("append", [True, False])265 @pytest.mark.parametrize("drop", [True, False])266 def test_set_index_raise_on_len(267 self, frame_of_index_cols, box, length, drop, append268 ):269 # GH 24984270 df = frame_of_index_cols # has length 5271 values = np.random.randint(0, 10, (length,))272 msg = "Length mismatch: Expected 5 rows, received array of length.*"273 # wrong length directly274 with pytest.raises(ValueError, match=msg):275 df.set_index(box(values), drop=drop, append=append)276 # wrong length in list277 with pytest.raises(ValueError, match=msg):278 df.set_index(["A", df.A, box(values)], drop=drop, append=append)279 def test_set_index_custom_label_type(self):280 # GH 24969281 class Thing:282 def __init__(self, name, color):283 self.name = name284 self.color = color285 def __str__(self):286 return "<Thing {self.name!r}>".format(self=self)287 # necessary for pretty KeyError288 __repr__ = __str__289 thing1 = Thing("One", "red")290 thing2 = Thing("Two", "blue")291 df = DataFrame({thing1: [0, 1], thing2: [2, 3]})292 expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))293 # use custom label directly294 result = df.set_index(thing2)295 tm.assert_frame_equal(result, expected)296 # custom label wrapped in list297 result = df.set_index([thing2])298 tm.assert_frame_equal(result, expected)299 # missing key300 thing3 = Thing("Three", "pink")301 msg = "<Thing 'Three'>"302 with pytest.raises(KeyError, match=msg):303 # missing label directly304 df.set_index(thing3)305 with pytest.raises(KeyError, match=msg):306 # missing label in list307 df.set_index([thing3])308 def test_set_index_custom_label_hashable_iterable(self):309 # GH 24969310 # actual example discussed in GH 24984 was e.g. for shapely.geometry311 # objects (e.g. a collection of Points) that can be both hashable and312 # iterable; using frozenset as a stand-in for testing here313 class Thing(frozenset):314 # need to stabilize repr for KeyError (due to random order in sets)315 def __repr__(self):316 tmp = sorted(list(self))317 # double curly brace prints one brace in format string318 return "frozenset({{{}}})".format(", ".join(map(repr, tmp)))319 thing1 = Thing(["One", "red"])320 thing2 = Thing(["Two", "blue"])321 df = DataFrame({thing1: [0, 1], thing2: [2, 3]})322 expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2))323 # use custom label directly324 result = df.set_index(thing2)325 tm.assert_frame_equal(result, expected)326 # custom label wrapped in list327 result = df.set_index([thing2])328 tm.assert_frame_equal(result, expected)329 # missing key330 thing3 = Thing(["Three", "pink"])331 msg = r"frozenset\(\{'Three', 'pink'\}\)"332 with pytest.raises(KeyError, match=msg):333 # missing label directly334 df.set_index(thing3)335 with pytest.raises(KeyError, match=msg):336 # missing label in list337 df.set_index([thing3])338 def test_set_index_custom_label_type_raises(self):339 # GH 24969340 # purposefully inherit from something unhashable341 class Thing(set):342 def __init__(self, name, color):343 self.name = name344 self.color = color345 def __str__(self):346 return "<Thing {self.name!r}>".format(self=self)347 thing1 = Thing("One", "red")348 thing2 = Thing("Two", "blue")349 df = DataFrame([[0, 2], [1, 3]], columns=[thing1, thing2])350 msg = 'The parameter "keys" may be a column key, .*'351 with pytest.raises(TypeError, match=msg):352 # use custom label directly353 df.set_index(thing2)354 with pytest.raises(TypeError, match=msg):355 # custom label wrapped in list356 df.set_index([thing2])357 def test_construction_with_categorical_index(self):358 ci = tm.makeCategoricalIndex(10)359 ci.name = "B"360 # with Categorical361 df = DataFrame({"A": np.random.randn(10), "B": ci.values})362 idf = df.set_index("B")363 tm.assert_index_equal(idf.index, ci)364 # from a CategoricalIndex365 df = DataFrame({"A": np.random.randn(10), "B": ci})366 idf = df.set_index("B")367 tm.assert_index_equal(idf.index, ci)368 # round-trip369 idf = idf.reset_index().set_index("B")370 tm.assert_index_equal(idf.index, ci)371 def test_set_index_cast_datetimeindex(self):372 df = DataFrame(373 {374 "A": [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)],375 "B": np.random.randn(1000),376 }377 )378 idf = df.set_index("A")379 assert isinstance(idf.index, DatetimeIndex)380 def test_convert_dti_to_series(self):381 # don't cast a DatetimeIndex WITH a tz, leave as object382 # GH 6032383 idx = DatetimeIndex(384 to_datetime(["2013-1-1 13:00", "2013-1-2 14:00"]), name="B"385 ).tz_localize("US/Pacific")386 df = DataFrame(np.random.randn(2, 1), columns=["A"])387 expected = Series(388 np.array(389 [390 Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),391 Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),392 ],393 dtype="object",394 ),395 name="B",396 )397 # convert index to series398 result = Series(idx)399 tm.assert_series_equal(result, expected)400 # assign to frame401 df["B"] = idx402 result = df["B"]403 tm.assert_series_equal(result, expected)404 # convert to series while keeping the timezone405 result = idx.to_series(keep_tz=True, index=[0, 1])406 tm.assert_series_equal(result, expected)407 # convert to utc408 with tm.assert_produces_warning(FutureWarning):409 df["B"] = idx.to_series(keep_tz=False, index=[0, 1])410 result = df["B"]411 comp = Series(DatetimeIndex(expected.values).tz_localize(None), name="B")412 tm.assert_series_equal(result, comp)413 with tm.assert_produces_warning(FutureWarning) as m:414 result = idx.to_series(index=[0, 1])415 tm.assert_series_equal(result, expected.dt.tz_convert(None))416 msg = (417 "The default of the 'keep_tz' keyword in "418 "DatetimeIndex.to_series will change to True in a future "419 "release."420 )421 assert msg in str(m[0].message)422 with tm.assert_produces_warning(FutureWarning):423 result = idx.to_series(keep_tz=False, index=[0, 1])424 tm.assert_series_equal(result, expected.dt.tz_convert(None))425 # list of datetimes with a tz426 df["B"] = idx.to_pydatetime()427 result = df["B"]428 tm.assert_series_equal(result, expected)429 # GH 6785430 # set the index manually431 import pytz432 df = DataFrame([{"ts": datetime(2014, 4, 1, tzinfo=pytz.utc), "foo": 1}])433 expected = df.set_index("ts")434 df.index = df["ts"]435 df.pop("ts")436 tm.assert_frame_equal(df, expected)437 def test_reset_index_tz(self, tz_aware_fixture):438 # GH 3950439 # reset_index with single level440 tz = tz_aware_fixture441 idx = date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx")442 df = DataFrame({"a": range(5), "b": ["A", "B", "C", "D", "E"]}, index=idx)443 expected = DataFrame(444 {445 "idx": [446 datetime(2011, 1, 1),447 datetime(2011, 1, 2),448 datetime(2011, 1, 3),449 datetime(2011, 1, 4),450 datetime(2011, 1, 5),451 ],452 "a": range(5),453 "b": ["A", "B", "C", "D", "E"],454 },455 columns=["idx", "a", "b"],456 )457 expected["idx"] = expected["idx"].apply(lambda d: Timestamp(d, tz=tz))458 tm.assert_frame_equal(df.reset_index(), expected)459 def test_set_index_timezone(self):460 # GH 12358461 # tz-aware Series should retain the tz462 idx = to_datetime(["2014-01-01 10:10:10"], utc=True).tz_convert("Europe/Rome")463 df = DataFrame({"A": idx})464 assert df.set_index(idx).index[0].hour == 11465 assert DatetimeIndex(Series(df.A))[0].hour == 11466 assert df.set_index(df.A).index[0].hour == 11467 def test_set_index_dst(self):468 di = date_range("2006-10-29 00:00:00", periods=3, freq="H", tz="US/Pacific")469 df = DataFrame(data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=di).reset_index()470 # single level471 res = df.set_index("index")472 exp = DataFrame(473 data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=Index(di, name="index")474 )475 tm.assert_frame_equal(res, exp)476 # GH 12920477 res = df.set_index(["index", "a"])478 exp_index = MultiIndex.from_arrays([di, [0, 1, 2]], names=["index", "a"])479 exp = DataFrame({"b": [3, 4, 5]}, index=exp_index)480 tm.assert_frame_equal(res, exp)481 def test_reset_index_with_intervals(self):482 idx = IntervalIndex.from_breaks(np.arange(11), name="x")483 original = DataFrame({"x": idx, "y": np.arange(10)})[["x", "y"]]484 result = original.set_index("x")485 expected = DataFrame({"y": np.arange(10)}, index=idx)486 tm.assert_frame_equal(result, expected)487 result2 = result.reset_index()488 tm.assert_frame_equal(result2, original)489 def test_set_index_multiindexcolumns(self):490 columns = MultiIndex.from_tuples([("foo", 1), ("foo", 2), ("bar", 1)])491 df = DataFrame(np.random.randn(3, 3), columns=columns)492 result = df.set_index(df.columns[0])493 expected = df.iloc[:, 1:]494 expected.index = df.iloc[:, 0].values495 expected.index.names = [df.columns[0]]496 tm.assert_frame_equal(result, expected)497 def test_set_index_empty_column(self):498 # GH 1971499 df = DataFrame(500 [501 {"a": 1, "p": 0},502 {"a": 2, "m": 10},503 {"a": 3, "m": 11, "p": 20},504 {"a": 4, "m": 12, "p": 21},505 ],506 columns=("a", "m", "p", "x"),507 )508 result = df.set_index(["a", "x"])509 expected = df[["m", "p"]]510 expected.index = MultiIndex.from_arrays([df["a"], df["x"]], names=["a", "x"])511 tm.assert_frame_equal(result, expected)512 def test_set_columns(self, float_string_frame):513 cols = Index(np.arange(len(float_string_frame.columns)))514 float_string_frame.columns = cols515 with pytest.raises(ValueError, match="Length mismatch"):516 float_string_frame.columns = cols[::2]517 def test_dti_set_index_reindex(self):518 # GH 6631519 df = DataFrame(np.random.random(6))520 idx1 = date_range("2011/01/01", periods=6, freq="M", tz="US/Eastern")521 idx2 = date_range("2013", periods=6, freq="A", tz="Asia/Tokyo")522 df = df.set_index(idx1)523 tm.assert_index_equal(df.index, idx1)524 df = df.reindex(idx2)525 tm.assert_index_equal(df.index, idx2)526 # GH 11314527 # with tz528 index = date_range(529 datetime(2015, 10, 1), datetime(2015, 10, 1, 23), freq="H", tz="US/Eastern"530 )531 df = DataFrame(np.random.randn(24, 1), columns=["a"], index=index)532 new_index = date_range(533 datetime(2015, 10, 2), datetime(2015, 10, 2, 23), freq="H", tz="US/Eastern"534 )535 result = df.set_index(new_index)536 assert result.index.freq == index.freq537 # Renaming538 def test_rename(self, float_frame):539 mapping = {"A": "a", "B": "b", "C": "c", "D": "d"}540 renamed = float_frame.rename(columns=mapping)541 renamed2 = float_frame.rename(columns=str.lower)542 tm.assert_frame_equal(renamed, renamed2)543 tm.assert_frame_equal(544 renamed2.rename(columns=str.upper), float_frame, check_names=False545 )546 # index547 data = {"A": {"foo": 0, "bar": 1}}548 # gets sorted alphabetical549 df = DataFrame(data)550 renamed = df.rename(index={"foo": "bar", "bar": "foo"})551 tm.assert_index_equal(renamed.index, Index(["foo", "bar"]))552 renamed = df.rename(index=str.upper)553 tm.assert_index_equal(renamed.index, Index(["BAR", "FOO"]))554 # have to pass something555 with pytest.raises(TypeError, match="must pass an index to rename"):556 float_frame.rename()557 # partial columns558 renamed = float_frame.rename(columns={"C": "foo", "D": "bar"})559 tm.assert_index_equal(renamed.columns, Index(["A", "B", "foo", "bar"]))560 # other axis561 renamed = float_frame.T.rename(index={"C": "foo", "D": "bar"})562 tm.assert_index_equal(renamed.index, Index(["A", "B", "foo", "bar"]))563 # index with name564 index = Index(["foo", "bar"], name="name")565 renamer = DataFrame(data, index=index)566 renamed = renamer.rename(index={"foo": "bar", "bar": "foo"})567 tm.assert_index_equal(renamed.index, Index(["bar", "foo"], name="name"))568 assert renamed.index.name == renamer.index.name569 def test_rename_axis_inplace(self, float_frame):570 # GH 15704571 expected = float_frame.rename_axis("foo")572 result = float_frame.copy()573 no_return = result.rename_axis("foo", inplace=True)574 assert no_return is None575 tm.assert_frame_equal(result, expected)576 expected = float_frame.rename_axis("bar", axis=1)577 result = float_frame.copy()578 no_return = result.rename_axis("bar", axis=1, inplace=True)579 assert no_return is None580 tm.assert_frame_equal(result, expected)581 def test_rename_axis_raises(self):582 # https://github.com/pandas-dev/pandas/issues/17833583 df = DataFrame({"A": [1, 2], "B": [1, 2]})584 with pytest.raises(ValueError, match="Use `.rename`"):585 df.rename_axis(id, axis=0)586 with pytest.raises(ValueError, match="Use `.rename`"):587 df.rename_axis({0: 10, 1: 20}, axis=0)588 with pytest.raises(ValueError, match="Use `.rename`"):589 df.rename_axis(id, axis=1)590 with pytest.raises(ValueError, match="Use `.rename`"):591 df["A"].rename_axis(id)592 def test_rename_axis_mapper(self):593 # GH 19978594 mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])595 df = DataFrame(596 {"x": [i for i in range(len(mi))], "y": [i * 10 for i in range(len(mi))]},597 index=mi,598 )599 # Test for rename of the Index object of columns600 result = df.rename_axis("cols", axis=1)601 tm.assert_index_equal(result.columns, Index(["x", "y"], name="cols"))602 # Test for rename of the Index object of columns using dict603 result = result.rename_axis(columns={"cols": "new"}, axis=1)604 tm.assert_index_equal(result.columns, Index(["x", "y"], name="new"))605 # Test for renaming index using dict606 result = df.rename_axis(index={"ll": "foo"})607 assert result.index.names == ["foo", "nn"]608 # Test for renaming index using a function609 result = df.rename_axis(index=str.upper, axis=0)610 assert result.index.names == ["LL", "NN"]611 # Test for renaming index providing complete list612 result = df.rename_axis(index=["foo", "goo"])613 assert result.index.names == ["foo", "goo"]614 # Test for changing index and columns at same time615 sdf = df.reset_index().set_index("nn").drop(columns=["ll", "y"])616 result = sdf.rename_axis(index="foo", columns="meh")617 assert result.index.name == "foo"618 assert result.columns.name == "meh"619 # Test different error cases620 with pytest.raises(TypeError, match="Must pass"):621 df.rename_axis(index="wrong")622 with pytest.raises(ValueError, match="Length of names"):623 df.rename_axis(index=["wrong"])624 with pytest.raises(TypeError, match="bogus"):625 df.rename_axis(bogus=None)626 @pytest.mark.parametrize(627 "kwargs, rename_index, rename_columns",628 [629 ({"mapper": None, "axis": 0}, True, False),630 ({"mapper": None, "axis": 1}, False, True),631 ({"index": None}, True, False),632 ({"columns": None}, False, True),633 ({"index": None, "columns": None}, True, True),634 ({}, False, False),635 ],636 )637 def test_rename_axis_none(self, kwargs, rename_index, rename_columns):638 # GH 25034639 index = Index(list("abc"), name="foo")640 columns = Index(["col1", "col2"], name="bar")641 data = np.arange(6).reshape(3, 2)642 df = DataFrame(data, index, columns)643 result = df.rename_axis(**kwargs)644 expected_index = index.rename(None) if rename_index else index645 expected_columns = columns.rename(None) if rename_columns else columns646 expected = DataFrame(data, expected_index, expected_columns)647 tm.assert_frame_equal(result, expected)648 def test_rename_multiindex(self):649 tuples_index = [("foo1", "bar1"), ("foo2", "bar2")]650 tuples_columns = [("fizz1", "buzz1"), ("fizz2", "buzz2")]651 index = MultiIndex.from_tuples(tuples_index, names=["foo", "bar"])652 columns = MultiIndex.from_tuples(tuples_columns, names=["fizz", "buzz"])653 df = DataFrame([(0, 0), (1, 1)], index=index, columns=columns)654 #655 # without specifying level -> across all levels656 renamed = df.rename(657 index={"foo1": "foo3", "bar2": "bar3"},658 columns={"fizz1": "fizz3", "buzz2": "buzz3"},659 )660 new_index = MultiIndex.from_tuples(661 [("foo3", "bar1"), ("foo2", "bar3")], names=["foo", "bar"]662 )663 new_columns = MultiIndex.from_tuples(664 [("fizz3", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]665 )666 tm.assert_index_equal(renamed.index, new_index)667 tm.assert_index_equal(renamed.columns, new_columns)668 assert renamed.index.names == df.index.names669 assert renamed.columns.names == df.columns.names670 #671 # with specifying a level (GH13766)672 # dict673 new_columns = MultiIndex.from_tuples(674 [("fizz3", "buzz1"), ("fizz2", "buzz2")], names=["fizz", "buzz"]675 )676 renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=0)677 tm.assert_index_equal(renamed.columns, new_columns)678 renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="fizz")679 tm.assert_index_equal(renamed.columns, new_columns)680 new_columns = MultiIndex.from_tuples(681 [("fizz1", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]682 )683 renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=1)684 tm.assert_index_equal(renamed.columns, new_columns)685 renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="buzz")686 tm.assert_index_equal(renamed.columns, new_columns)687 # function688 func = str.upper689 new_columns = MultiIndex.from_tuples(690 [("FIZZ1", "buzz1"), ("FIZZ2", "buzz2")], names=["fizz", "buzz"]691 )692 renamed = df.rename(columns=func, level=0)693 tm.assert_index_equal(renamed.columns, new_columns)694 renamed = df.rename(columns=func, level="fizz")695 tm.assert_index_equal(renamed.columns, new_columns)696 new_columns = MultiIndex.from_tuples(697 [("fizz1", "BUZZ1"), ("fizz2", "BUZZ2")], names=["fizz", "buzz"]698 )699 renamed = df.rename(columns=func, level=1)700 tm.assert_index_equal(renamed.columns, new_columns)701 renamed = df.rename(columns=func, level="buzz")702 tm.assert_index_equal(renamed.columns, new_columns)703 # index704 new_index = MultiIndex.from_tuples(705 [("foo3", "bar1"), ("foo2", "bar2")], names=["foo", "bar"]706 )707 renamed = df.rename(index={"foo1": "foo3", "bar2": "bar3"}, level=0)708 tm.assert_index_equal(renamed.index, new_index)709 def test_rename_nocopy(self, float_frame):710 renamed = float_frame.rename(columns={"C": "foo"}, copy=False)711 renamed["foo"] = 1.0712 assert (float_frame["C"] == 1.0).all()713 def test_rename_inplace(self, float_frame):714 float_frame.rename(columns={"C": "foo"})715 assert "C" in float_frame716 assert "foo" not in float_frame717 c_id = id(float_frame["C"])718 float_frame = float_frame.copy()719 float_frame.rename(columns={"C": "foo"}, inplace=True)720 assert "C" not in float_frame721 assert "foo" in float_frame722 assert id(float_frame["foo"]) != c_id723 def test_rename_bug(self):724 # GH 5344725 # rename set ref_locs, and set_index was not resetting726 df = DataFrame({0: ["foo", "bar"], 1: ["bah", "bas"], 2: [1, 2]})727 df = df.rename(columns={0: "a"})728 df = df.rename(columns={1: "b"})729 df = df.set_index(["a", "b"])730 df.columns = ["2001-01-01"]731 expected = DataFrame(732 [[1], [2]],733 index=MultiIndex.from_tuples(734 [("foo", "bah"), ("bar", "bas")], names=["a", "b"]735 ),736 columns=["2001-01-01"],737 )738 tm.assert_frame_equal(df, expected)739 def test_rename_bug2(self):740 # GH 19497741 # rename was changing Index to MultiIndex if Index contained tuples742 df = DataFrame(data=np.arange(3), index=[(0, 0), (1, 1), (2, 2)], columns=["a"])743 df = df.rename({(1, 1): (5, 4)}, axis="index")744 expected = DataFrame(745 data=np.arange(3), index=[(0, 0), (5, 4), (2, 2)], columns=["a"]746 )747 tm.assert_frame_equal(df, expected)748 def test_rename_errors_raises(self):749 df = DataFrame(columns=["A", "B", "C", "D"])750 with pytest.raises(KeyError, match="'E'] not found in axis"):751 df.rename(columns={"A": "a", "E": "e"}, errors="raise")752 @pytest.mark.parametrize(753 "mapper, errors, expected_columns",754 [755 ({"A": "a", "E": "e"}, "ignore", ["a", "B", "C", "D"]),756 ({"A": "a"}, "raise", ["a", "B", "C", "D"]),757 (str.lower, "raise", ["a", "b", "c", "d"]),758 ],759 )760 def test_rename_errors(self, mapper, errors, expected_columns):761 # GH 13473762 # rename now works with errors parameter763 df = DataFrame(columns=["A", "B", "C", "D"])764 result = df.rename(columns=mapper, errors=errors)765 expected = DataFrame(columns=expected_columns)766 tm.assert_frame_equal(result, expected)767 def test_reorder_levels(self):768 index = MultiIndex(769 levels=[["bar"], ["one", "two", "three"], [0, 1]],770 codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],771 names=["L0", "L1", "L2"],772 )773 df = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=index)774 # no change, position775 result = df.reorder_levels([0, 1, 2])776 tm.assert_frame_equal(df, result)777 # no change, labels778 result = df.reorder_levels(["L0", "L1", "L2"])779 tm.assert_frame_equal(df, result)780 # rotate, position781 result = df.reorder_levels([1, 2, 0])782 e_idx = MultiIndex(783 levels=[["one", "two", "three"], [0, 1], ["bar"]],784 codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0]],785 names=["L1", "L2", "L0"],786 )787 expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)788 tm.assert_frame_equal(result, expected)789 result = df.reorder_levels([0, 0, 0])790 e_idx = MultiIndex(791 levels=[["bar"], ["bar"], ["bar"]],792 codes=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]],793 names=["L0", "L0", "L0"],794 )795 expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)796 tm.assert_frame_equal(result, expected)797 result = df.reorder_levels(["L0", "L0", "L0"])798 tm.assert_frame_equal(result, expected)799 def test_reset_index(self, float_frame):800 stacked = float_frame.stack()[::2]801 stacked = DataFrame({"foo": stacked, "bar": stacked})802 names = ["first", "second"]803 stacked.index.names = names804 deleveled = stacked.reset_index()805 for i, (lev, level_codes) in enumerate(806 zip(stacked.index.levels, stacked.index.codes)807 ):808 values = lev.take(level_codes)809 name = names[i]810 tm.assert_index_equal(values, Index(deleveled[name]))811 stacked.index.names = [None, None]812 deleveled2 = stacked.reset_index()813 tm.assert_series_equal(814 deleveled["first"], deleveled2["level_0"], check_names=False815 )816 tm.assert_series_equal(817 deleveled["second"], deleveled2["level_1"], check_names=False818 )819 # default name assigned820 rdf = float_frame.reset_index()821 exp = Series(float_frame.index.values, name="index")822 tm.assert_series_equal(rdf["index"], exp)823 # default name assigned, corner case824 df = float_frame.copy()825 df["index"] = "foo"826 rdf = df.reset_index()827 exp = Series(float_frame.index.values, name="level_0")828 tm.assert_series_equal(rdf["level_0"], exp)829 # but this is ok830 float_frame.index.name = "index"831 deleveled = float_frame.reset_index()832 tm.assert_series_equal(deleveled["index"], Series(float_frame.index))833 tm.assert_index_equal(deleveled.index, Index(np.arange(len(deleveled))))834 # preserve column names835 float_frame.columns.name = "columns"836 resetted = float_frame.reset_index()837 assert resetted.columns.name == "columns"838 # only remove certain columns839 df = float_frame.reset_index().set_index(["index", "A", "B"])840 rs = df.reset_index(["A", "B"])841 # TODO should reset_index check_names ?842 tm.assert_frame_equal(rs, float_frame, check_names=False)843 rs = df.reset_index(["index", "A", "B"])844 tm.assert_frame_equal(rs, float_frame.reset_index(), check_names=False)845 rs = df.reset_index(["index", "A", "B"])846 tm.assert_frame_equal(rs, float_frame.reset_index(), check_names=False)847 rs = df.reset_index("A")848 xp = float_frame.reset_index().set_index(["index", "B"])849 tm.assert_frame_equal(rs, xp, check_names=False)850 # test resetting in place851 df = float_frame.copy()852 resetted = float_frame.reset_index()853 df.reset_index(inplace=True)854 tm.assert_frame_equal(df, resetted, check_names=False)855 df = float_frame.reset_index().set_index(["index", "A", "B"])856 rs = df.reset_index("A", drop=True)857 xp = float_frame.copy()858 del xp["A"]859 xp = xp.set_index(["B"], append=True)860 tm.assert_frame_equal(rs, xp, check_names=False)861 def test_reset_index_name(self):862 df = DataFrame(863 [[1, 2, 3, 4], [5, 6, 7, 8]],864 columns=["A", "B", "C", "D"],865 index=Index(range(2), name="x"),866 )867 assert df.reset_index().index.name is None868 assert df.reset_index(drop=True).index.name is None869 df.reset_index(inplace=True)870 assert df.index.name is None871 def test_reset_index_level(self):872 df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "C", "D"])873 for levels in ["A", "B"], [0, 1]:874 # With MultiIndex875 result = df.set_index(["A", "B"]).reset_index(level=levels[0])876 tm.assert_frame_equal(result, df.set_index("B"))877 result = df.set_index(["A", "B"]).reset_index(level=levels[:1])878 tm.assert_frame_equal(result, df.set_index("B"))879 result = df.set_index(["A", "B"]).reset_index(level=levels)880 tm.assert_frame_equal(result, df)881 result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True)882 tm.assert_frame_equal(result, df[["C", "D"]])883 # With single-level Index (GH 16263)884 result = df.set_index("A").reset_index(level=levels[0])885 tm.assert_frame_equal(result, df)886 result = df.set_index("A").reset_index(level=levels[:1])887 tm.assert_frame_equal(result, df)888 result = df.set_index(["A"]).reset_index(level=levels[0], drop=True)889 tm.assert_frame_equal(result, df[["B", "C", "D"]])890 # Missing levels - for both MultiIndex and single-level Index:891 for idx_lev in ["A", "B"], ["A"]:892 with pytest.raises(KeyError, match="Level E "):893 df.set_index(idx_lev).reset_index(level=["A", "E"])894 with pytest.raises(IndexError, match="Too many levels"):895 df.set_index(idx_lev).reset_index(level=[0, 1, 2])896 def test_reset_index_right_dtype(self):897 time = np.arange(0.0, 10, np.sqrt(2) / 2)898 s1 = Series(899 (9.81 * time ** 2) / 2, index=Index(time, name="time"), name="speed"900 )901 df = DataFrame(s1)902 resetted = s1.reset_index()903 assert resetted["time"].dtype == np.float64904 resetted = df.reset_index()905 assert resetted["time"].dtype == np.float64906 def test_reset_index_multiindex_col(self):907 vals = np.random.randn(3, 3).astype(object)908 idx = ["x", "y", "z"]909 full = np.hstack(([[x] for x in idx], vals))910 df = DataFrame(911 vals,912 Index(idx, name="a"),913 columns=[["b", "b", "c"], ["mean", "median", "mean"]],914 )915 rs = df.reset_index()916 xp = DataFrame(917 full, columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]]918 )919 tm.assert_frame_equal(rs, xp)920 rs = df.reset_index(col_fill=None)921 xp = DataFrame(922 full, columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]]923 )924 tm.assert_frame_equal(rs, xp)925 rs = df.reset_index(col_level=1, col_fill="blah")926 xp = DataFrame(927 full, columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]]928 )929 tm.assert_frame_equal(rs, xp)930 df = DataFrame(931 vals,932 MultiIndex.from_arrays([[0, 1, 2], ["x", "y", "z"]], names=["d", "a"]),933 columns=[["b", "b", "c"], ["mean", "median", "mean"]],934 )935 rs = df.reset_index("a")936 xp = DataFrame(937 full,938 Index([0, 1, 2], name="d"),939 columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]],940 )941 tm.assert_frame_equal(rs, xp)942 rs = df.reset_index("a", col_fill=None)943 xp = DataFrame(944 full,945 Index(range(3), name="d"),946 columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]],947 )948 tm.assert_frame_equal(rs, xp)949 rs = df.reset_index("a", col_fill="blah", col_level=1)950 xp = DataFrame(951 full,952 Index(range(3), name="d"),953 columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]],954 )955 tm.assert_frame_equal(rs, xp)956 def test_reset_index_multiindex_nan(self):957 # GH6322, testing reset_index on MultiIndexes958 # when we have a nan or all nan959 df = DataFrame(960 {"A": ["a", "b", "c"], "B": [0, 1, np.nan], "C": np.random.rand(3)}961 )962 rs = df.set_index(["A", "B"]).reset_index()963 tm.assert_frame_equal(rs, df)964 df = DataFrame(965 {"A": [np.nan, "b", "c"], "B": [0, 1, 2], "C": np.random.rand(3)}966 )967 rs = df.set_index(["A", "B"]).reset_index()968 tm.assert_frame_equal(rs, df)969 df = DataFrame({"A": ["a", "b", "c"], "B": [0, 1, 2], "C": [np.nan, 1.1, 2.2]})970 rs = df.set_index(["A", "B"]).reset_index()971 tm.assert_frame_equal(rs, df)972 df = DataFrame(973 {974 "A": ["a", "b", "c"],975 "B": [np.nan, np.nan, np.nan],976 "C": np.random.rand(3),977 }978 )979 rs = df.set_index(["A", "B"]).reset_index()980 tm.assert_frame_equal(rs, df)981 def test_reset_index_with_datetimeindex_cols(self):982 # GH5818983 #984 df = DataFrame(985 [[1, 2], [3, 4]],986 columns=date_range("1/1/2013", "1/2/2013"),987 index=["A", "B"],988 )989 result = df.reset_index()990 expected = DataFrame(991 [["A", 1, 2], ["B", 3, 4]],992 columns=["index", datetime(2013, 1, 1), datetime(2013, 1, 2)],993 )994 tm.assert_frame_equal(result, expected)995 def test_reset_index_range(self):996 # GH 12071997 df = DataFrame([[0, 0], [1, 1]], columns=["A", "B"], index=RangeIndex(stop=2))998 result = df.reset_index()999 assert isinstance(result.index, RangeIndex)1000 expected = DataFrame(1001 [[0, 0, 0], [1, 1, 1]],1002 columns=["index", "A", "B"],1003 index=RangeIndex(stop=2),1004 )1005 tm.assert_frame_equal(result, expected)1006 def test_set_index_names(self):1007 df = tm.makeDataFrame()1008 df.index.name = "name"1009 assert df.set_index(df.index).index.names == ["name"]1010 mi = MultiIndex.from_arrays(df[["A", "B"]].T.values, names=["A", "B"])1011 mi2 = MultiIndex.from_arrays(1012 df[["A", "B", "A", "B"]].T.values, names=["A", "B", "C", "D"]1013 )1014 df = df.set_index(["A", "B"])1015 assert df.set_index(df.index).index.names == ["A", "B"]1016 # Check that set_index isn't converting a MultiIndex into an Index1017 assert isinstance(df.set_index(df.index).index, MultiIndex)1018 # Check actual equality1019 tm.assert_index_equal(df.set_index(df.index).index, mi)1020 idx2 = df.index.rename(["C", "D"])1021 # Check that [MultiIndex, MultiIndex] yields a MultiIndex rather1022 # than a pair of tuples1023 assert isinstance(df.set_index([df.index, idx2]).index, MultiIndex)1024 # Check equality1025 tm.assert_index_equal(df.set_index([df.index, idx2]).index, mi2)1026 def test_rename_objects(self, float_string_frame):1027 renamed = float_string_frame.rename(columns=str.upper)1028 assert "FOO" in renamed1029 assert "foo" not in renamed1030 def test_rename_axis_style(self):1031 # https://github.com/pandas-dev/pandas/issues/123921032 df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["X", "Y"])1033 expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])1034 result = df.rename(str.lower, axis=1)1035 tm.assert_frame_equal(result, expected)1036 result = df.rename(str.lower, axis="columns")1037 tm.assert_frame_equal(result, expected)1038 result = df.rename({"A": "a", "B": "b"}, axis=1)1039 tm.assert_frame_equal(result, expected)1040 result = df.rename({"A": "a", "B": "b"}, axis="columns")1041 tm.assert_frame_equal(result, expected)1042 # Index1043 expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])1044 result = df.rename(str.lower, axis=0)1045 tm.assert_frame_equal(result, expected)1046 result = df.rename(str.lower, axis="index")1047 tm.assert_frame_equal(result, expected)1048 result = df.rename({"X": "x", "Y": "y"}, axis=0)1049 tm.assert_frame_equal(result, expected)1050 result = df.rename({"X": "x", "Y": "y"}, axis="index")1051 tm.assert_frame_equal(result, expected)1052 result = df.rename(mapper=str.lower, axis="index")1053 tm.assert_frame_equal(result, expected)1054 def test_rename_mapper_multi(self):1055 df = DataFrame({"A": ["a", "b"], "B": ["c", "d"], "C": [1, 2]}).set_index(1056 ["A", "B"]1057 )1058 result = df.rename(str.upper)1059 expected = df.rename(index=str.upper)1060 tm.assert_frame_equal(result, expected)1061 def test_rename_positional_named(self):1062 # https://github.com/pandas-dev/pandas/issues/123921063 df = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])1064 result = df.rename(str.lower, columns=str.upper)1065 expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])1066 tm.assert_frame_equal(result, expected)1067 def test_rename_axis_style_raises(self):1068 # see gh-123921069 df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["0", "1"])1070 # Named target and axis1071 over_spec_msg = "Cannot specify both 'axis' and any of 'index' or 'columns'"1072 with pytest.raises(TypeError, match=over_spec_msg):1073 df.rename(index=str.lower, axis=1)1074 with pytest.raises(TypeError, match=over_spec_msg):1075 df.rename(index=str.lower, axis="columns")1076 with pytest.raises(TypeError, match=over_spec_msg):1077 df.rename(columns=str.lower, axis="columns")1078 with pytest.raises(TypeError, match=over_spec_msg):1079 df.rename(index=str.lower, axis=0)1080 # Multiple targets and axis1081 with pytest.raises(TypeError, match=over_spec_msg):1082 df.rename(str.lower, str.lower, axis="columns")1083 # Too many targets1084 over_spec_msg = "Cannot specify all of 'mapper', 'index', 'columns'."1085 with pytest.raises(TypeError, match=over_spec_msg):1086 df.rename(str.lower, str.lower, str.lower)1087 # Duplicates1088 with pytest.raises(TypeError, match="multiple values"):1089 df.rename(id, mapper=id)1090 def test_reindex_api_equivalence(self):1091 # equivalence of the labels/axis and index/columns API's1092 df = DataFrame(1093 [[1, 2, 3], [3, 4, 5], [5, 6, 7]],1094 index=["a", "b", "c"],1095 columns=["d", "e", "f"],1096 )1097 res1 = df.reindex(["b", "a"])1098 res2 = df.reindex(index=["b", "a"])1099 res3 = df.reindex(labels=["b", "a"])1100 res4 = df.reindex(labels=["b", "a"], axis=0)1101 res5 = df.reindex(["b", "a"], axis=0)1102 for res in [res2, res3, res4, res5]:1103 tm.assert_frame_equal(res1, res)1104 res1 = df.reindex(columns=["e", "d"])1105 res2 = df.reindex(["e", "d"], axis=1)1106 res3 = df.reindex(labels=["e", "d"], axis=1)1107 for res in [res2, res3]:1108 tm.assert_frame_equal(res1, res)1109 res1 = df.reindex(index=["b", "a"], columns=["e", "d"])1110 res2 = df.reindex(columns=["e", "d"], index=["b", "a"])1111 res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1)1112 for res in [res2, res3]:1113 tm.assert_frame_equal(res1, res)1114 def test_rename_positional(self):1115 df = DataFrame(columns=["A", "B"])1116 with tm.assert_produces_warning(FutureWarning) as rec:1117 result = df.rename(None, str.lower)1118 expected = DataFrame(columns=["a", "b"])1119 tm.assert_frame_equal(result, expected)1120 assert len(rec) == 11121 message = str(rec[0].message)1122 assert "rename" in message1123 assert "Use named arguments" in message1124 def test_assign_columns(self, float_frame):1125 float_frame["hi"] = "there"1126 df = float_frame.copy()1127 df.columns = ["foo", "bar", "baz", "quux", "foo2"]1128 tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False)1129 tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False)1130 def test_set_index_preserve_categorical_dtype(self):1131 # GH13743, GH138541132 df = DataFrame(1133 {1134 "A": [1, 2, 1, 1, 2],1135 "B": [10, 16, 22, 28, 34],1136 "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),1137 "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),1138 }1139 )1140 for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]:1141 result = df.set_index(cols).reset_index()1142 result = result.reindex(columns=df.columns)1143 tm.assert_frame_equal(result, df)1144 def test_ambiguous_warns(self):1145 df = DataFrame({"A": [1, 2]})1146 with tm.assert_produces_warning(FutureWarning):1147 df.rename(id, id)1148 with tm.assert_produces_warning(FutureWarning):1149 df.rename({0: 10}, {"A": "B"})1150 def test_rename_signature(self):1151 sig = inspect.signature(DataFrame.rename)1152 parameters = set(sig.parameters)1153 assert parameters == {1154 "self",1155 "mapper",1156 "index",1157 "columns",1158 "axis",1159 "inplace",1160 "copy",1161 "level",1162 "errors",1163 }1164 def test_reindex_signature(self):1165 sig = inspect.signature(DataFrame.reindex)1166 parameters = set(sig.parameters)1167 assert parameters == {1168 "self",1169 "labels",1170 "index",1171 "columns",1172 "axis",1173 "limit",1174 "copy",1175 "level",1176 "method",1177 "fill_value",1178 "tolerance",1179 }1180 def test_droplevel(self):1181 # GH203421182 df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])1183 df = df.set_index([0, 1]).rename_axis(["a", "b"])1184 df.columns = MultiIndex.from_tuples(1185 [("c", "e"), ("d", "f")], names=["level_1", "level_2"]1186 )1187 # test that dropping of a level in index works1188 expected = df.reset_index("a", drop=True)1189 result = df.droplevel("a", axis="index")1190 tm.assert_frame_equal(result, expected)1191 # test that dropping of a level in columns works1192 expected = df.copy()1193 expected.columns = Index(["c", "d"], name="level_1")1194 result = df.droplevel("level_2", axis="columns")1195 tm.assert_frame_equal(result, expected)1196@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")1197class TestIntervalIndex:1198 def test_setitem(self):1199 df = DataFrame({"A": range(10)})1200 s = cut(df.A, 5)1201 assert isinstance(s.cat.categories, IntervalIndex)1202 # B & D end up as Categoricals1203 # the remainer are converted to in-line objects1204 # contining an IntervalIndex.values1205 df["B"] = s1206 df["C"] = np.array(s)1207 df["D"] = s.values1208 df["E"] = np.array(s.values)1209 assert is_categorical_dtype(df["B"])1210 assert is_interval_dtype(df["B"].cat.categories)1211 assert is_categorical_dtype(df["D"])1212 assert is_interval_dtype(df["D"].cat.categories)1213 assert is_object_dtype(df["C"])1214 assert is_object_dtype(df["E"])1215 # they compare equal as Index1216 # when converted to numpy objects1217 c = lambda x: Index(np.array(x))1218 tm.assert_index_equal(c(df.B), c(df.B), check_names=False)1219 tm.assert_index_equal(c(df.B), c(df.C), check_names=False)1220 tm.assert_index_equal(c(df.B), c(df.D), check_names=False)1221 tm.assert_index_equal(c(df.B), c(df.D), check_names=False)1222 # B & D are the same Series1223 tm.assert_series_equal(df["B"], df["B"], check_names=False)1224 tm.assert_series_equal(df["B"], df["D"], check_names=False)1225 # C & E are the same Series1226 tm.assert_series_equal(df["C"], df["C"], check_names=False)1227 tm.assert_series_equal(df["C"], df["E"], check_names=False)1228 def test_set_reset_index(self):1229 df = DataFrame({"A": range(10)})1230 s = cut(df.A, 5)1231 df["B"] = s1232 df = df.set_index("B")1233 df = df.reset_index()1234 def test_set_axis_inplace(self):1235 # GH146361236 df = DataFrame(1237 {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2], "C": [4.4, 5.5, 6.6]},1238 index=[2010, 2011, 2012],1239 )1240 expected = {0: df.copy(), 1: df.copy()}1241 expected[0].index = list("abc")1242 expected[1].columns = list("abc")1243 expected["index"] = expected[0]1244 expected["columns"] = expected[1]1245 for axis in expected:1246 # inplace=True1247 # The FutureWarning comes from the fact that we would like to have...

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test_range.py

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...19 index=RangeIndex(0, 20, 2, name="foo"),20 index_dec=RangeIndex(18, -1, -2, name="bar"),21 )22 self.setup_indices()23 def create_index(self):24 return RangeIndex(5)25 def test_can_hold_identifiers(self):26 idx = self.create_index()27 key = idx[0]28 assert idx._can_hold_identifiers_and_holds_name(key) is False29 def test_too_many_names(self):30 with pytest.raises(ValueError, match="^Length"):31 self.index.names = ["roger", "harold"]32 @pytest.mark.parametrize("name", [None, "foo"])33 @pytest.mark.parametrize(34 "args, kwargs, start, stop, step",35 [36 ((5,), dict(), 0, 5, 1),37 ((1, 5), dict(), 1, 5, 1),38 ((1, 5, 2), dict(), 1, 5, 2),39 ((0,), dict(), 0, 0, 1),40 ((0, 0), dict(), 0, 0, 1),41 (tuple(), dict(start=0), 0, 0, 1),42 (tuple(), dict(stop=0), 0, 0, 1),43 ],44 )45 def test_constructor(self, args, kwargs, start, stop, step, name):46 result = RangeIndex(*args, name=name, **kwargs)47 expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)48 assert isinstance(result, RangeIndex)49 assert result.name is name50 assert result._range == range(start, stop, step)51 tm.assert_index_equal(result, expected)52 def test_constructor_invalid_args(self):53 msg = "RangeIndex\\(\\.\\.\\.\\) must be called with integers"54 with pytest.raises(TypeError, match=msg):55 RangeIndex()56 with pytest.raises(TypeError, match=msg):57 RangeIndex(name="Foo")58 # invalid args59 for i in [60 Index(["a", "b"]),61 Series(["a", "b"]),62 np.array(["a", "b"]),63 [],64 "foo",65 datetime(2000, 1, 1, 0, 0),66 np.arange(0, 10),67 np.array([1]),68 [1],69 ]:70 with pytest.raises(TypeError):71 RangeIndex(i)72 # we don't allow on a bare Index73 msg = (74 r"Index\(\.\.\.\) must be called with a collection of some "75 r"kind, 0 was passed"76 )77 with pytest.raises(TypeError, match=msg):78 Index(0, 1000)79 def test_constructor_same(self):80 # pass thru w and w/o copy81 index = RangeIndex(1, 5, 2)82 result = RangeIndex(index, copy=False)83 assert result.identical(index)84 result = RangeIndex(index, copy=True)85 tm.assert_index_equal(result, index, exact=True)86 result = RangeIndex(index)87 tm.assert_index_equal(result, index, exact=True)88 with pytest.raises(TypeError):89 RangeIndex(index, dtype="float64")90 def test_constructor_range(self):91 msg = "Value needs to be a scalar value, was type <class 'range'>"92 with pytest.raises(TypeError, match=msg):93 result = RangeIndex(range(1, 5, 2))94 result = RangeIndex.from_range(range(1, 5, 2))95 expected = RangeIndex(1, 5, 2)96 tm.assert_index_equal(result, expected, exact=True)97 result = RangeIndex.from_range(range(5, 6))98 expected = RangeIndex(5, 6, 1)99 tm.assert_index_equal(result, expected, exact=True)100 # an invalid range101 result = RangeIndex.from_range(range(5, 1))102 expected = RangeIndex(0, 0, 1)103 tm.assert_index_equal(result, expected, exact=True)104 result = RangeIndex.from_range(range(5))105 expected = RangeIndex(0, 5, 1)106 tm.assert_index_equal(result, expected, exact=True)107 result = Index(range(1, 5, 2))108 expected = RangeIndex(1, 5, 2)109 tm.assert_index_equal(result, expected, exact=True)110 with pytest.raises(TypeError):111 Index(range(1, 5, 2), dtype="float64")112 msg = r"^from_range\(\) got an unexpected keyword argument"113 with pytest.raises(TypeError, match=msg):114 pd.RangeIndex.from_range(range(10), copy=True)115 def test_constructor_name(self):116 # GH12288117 orig = RangeIndex(10)118 orig.name = "original"119 copy = RangeIndex(orig)120 copy.name = "copy"121 assert orig.name == "original"122 assert copy.name == "copy"123 new = Index(copy)124 assert new.name == "copy"125 new.name = "new"126 assert orig.name == "original"127 assert copy.name == "copy"128 assert new.name == "new"129 def test_constructor_corner(self):130 arr = np.array([1, 2, 3, 4], dtype=object)131 index = RangeIndex(1, 5)132 assert index.values.dtype == np.int64133 tm.assert_index_equal(index, Index(arr))134 # non-int raise Exception135 with pytest.raises(TypeError):136 RangeIndex("1", "10", "1")137 with pytest.raises(TypeError):138 RangeIndex(1.1, 10.2, 1.3)139 # invalid passed type140 with pytest.raises(TypeError):141 RangeIndex(1, 5, dtype="float64")142 @pytest.mark.parametrize(143 "index, start, stop, step",144 [145 (RangeIndex(5), 0, 5, 1),146 (RangeIndex(0, 5), 0, 5, 1),147 (RangeIndex(5, step=2), 0, 5, 2),148 (RangeIndex(1, 5, 2), 1, 5, 2),149 ],150 )151 def test_start_stop_step_attrs(self, index, start, stop, step):152 # GH 25710153 assert index.start == start154 assert index.stop == stop155 assert index.step == step156 @pytest.mark.parametrize("attr_name", ["_start", "_stop", "_step"])157 def test_deprecated_start_stop_step_attrs(self, attr_name):158 # GH 26581159 idx = self.create_index()160 with tm.assert_produces_warning(DeprecationWarning):161 getattr(idx, attr_name)162 def test_copy(self):163 i = RangeIndex(5, name="Foo")164 i_copy = i.copy()165 assert i_copy is not i166 assert i_copy.identical(i)167 assert i_copy._range == range(0, 5, 1)168 assert i_copy.name == "Foo"169 def test_repr(self):170 i = RangeIndex(5, name="Foo")171 result = repr(i)172 expected = "RangeIndex(start=0, stop=5, step=1, name='Foo')"173 assert result == expected174 result = eval(result)175 tm.assert_index_equal(result, i, exact=True)176 i = RangeIndex(5, 0, -1)177 result = repr(i)178 expected = "RangeIndex(start=5, stop=0, step=-1)"179 assert result == expected180 result = eval(result)181 tm.assert_index_equal(result, i, exact=True)182 def test_insert(self):183 idx = RangeIndex(5, name="Foo")184 result = idx[1:4]185 # test 0th element186 tm.assert_index_equal(idx[0:4], result.insert(0, idx[0]))187 # GH 18295 (test missing)188 expected = Float64Index([0, np.nan, 1, 2, 3, 4])189 for na in (np.nan, pd.NaT, None):190 result = RangeIndex(5).insert(1, na)191 tm.assert_index_equal(result, expected)192 def test_delete(self):193 idx = RangeIndex(5, name="Foo")194 expected = idx[1:].astype(int)195 result = idx.delete(0)196 tm.assert_index_equal(result, expected)197 assert result.name == expected.name198 expected = idx[:-1].astype(int)199 result = idx.delete(-1)200 tm.assert_index_equal(result, expected)201 assert result.name == expected.name202 with pytest.raises((IndexError, ValueError)):203 # either depending on numpy version204 result = idx.delete(len(idx))205 def test_view(self):206 i = RangeIndex(0, name="Foo")207 i_view = i.view()208 assert i_view.name == "Foo"209 i_view = i.view("i8")210 tm.assert_numpy_array_equal(i.values, i_view)211 i_view = i.view(RangeIndex)212 tm.assert_index_equal(i, i_view)213 def test_dtype(self):214 assert self.index.dtype == np.int64215 def test_cached_data(self):216 # GH 26565, GH26617217 # Calling RangeIndex._data caches an int64 array of the same length at218 # self._cached_data. This test checks whether _cached_data has been set219 idx = RangeIndex(0, 100, 10)220 assert idx._cached_data is None221 repr(idx)222 assert idx._cached_data is None223 str(idx)224 assert idx._cached_data is None225 idx.get_loc(20)226 assert idx._cached_data is None227 90 in idx228 assert idx._cached_data is None229 91 in idx230 assert idx._cached_data is None231 with tm.assert_produces_warning(FutureWarning):232 idx.contains(90)233 assert idx._cached_data is None234 with tm.assert_produces_warning(FutureWarning):235 idx.contains(91)236 assert idx._cached_data is None237 idx.all()238 assert idx._cached_data is None239 idx.any()240 assert idx._cached_data is None241 df = pd.DataFrame({"a": range(10)}, index=idx)242 df.loc[50]243 assert idx._cached_data is None244 with pytest.raises(KeyError, match="51"):245 df.loc[51]246 assert idx._cached_data is None247 df.loc[10:50]248 assert idx._cached_data is None249 df.iloc[5:10]250 assert idx._cached_data is None251 # actually calling idx._data252 assert isinstance(idx._data, np.ndarray)253 assert isinstance(idx._cached_data, np.ndarray)254 def test_is_monotonic(self):255 assert self.index.is_monotonic is True256 assert self.index.is_monotonic_increasing is True257 assert self.index.is_monotonic_decreasing is False258 assert self.index._is_strictly_monotonic_increasing is True259 assert self.index._is_strictly_monotonic_decreasing is False260 index = RangeIndex(4, 0, -1)261 assert index.is_monotonic is False262 assert index._is_strictly_monotonic_increasing is False263 assert index.is_monotonic_decreasing is True264 assert index._is_strictly_monotonic_decreasing is True265 index = RangeIndex(1, 2)266 assert index.is_monotonic is True267 assert index.is_monotonic_increasing is True268 assert index.is_monotonic_decreasing is True269 assert index._is_strictly_monotonic_increasing is True270 assert index._is_strictly_monotonic_decreasing is True271 index = RangeIndex(2, 1)272 assert index.is_monotonic is True273 assert index.is_monotonic_increasing is True274 assert index.is_monotonic_decreasing is True275 assert index._is_strictly_monotonic_increasing is True276 assert index._is_strictly_monotonic_decreasing is True277 index = RangeIndex(1, 1)278 assert index.is_monotonic is True279 assert index.is_monotonic_increasing is True280 assert index.is_monotonic_decreasing is True281 assert index._is_strictly_monotonic_increasing is True282 assert index._is_strictly_monotonic_decreasing is True283 def test_equals_range(self):284 equiv_pairs = [285 (RangeIndex(0, 9, 2), RangeIndex(0, 10, 2)),286 (RangeIndex(0), RangeIndex(1, -1, 3)),287 (RangeIndex(1, 2, 3), RangeIndex(1, 3, 4)),288 (RangeIndex(0, -9, -2), RangeIndex(0, -10, -2)),289 ]290 for left, right in equiv_pairs:291 assert left.equals(right)292 assert right.equals(left)293 def test_logical_compat(self):294 idx = self.create_index()295 assert idx.all() == idx.values.all()296 assert idx.any() == idx.values.any()297 def test_identical(self):298 i = Index(self.index.copy())299 assert i.identical(self.index)300 # we don't allow object dtype for RangeIndex301 if isinstance(self.index, RangeIndex):302 return303 same_values_different_type = Index(i, dtype=object)304 assert not i.identical(same_values_different_type)305 i = self.index.copy(dtype=object)306 i = i.rename("foo")307 same_values = Index(i, dtype=object)308 assert same_values.identical(self.index.copy(dtype=object))309 assert not i.identical(self.index)310 assert Index(same_values, name="foo", dtype=object).identical(i)311 assert not self.index.copy(dtype=object).identical(312 self.index.copy(dtype="int64")313 )314 def test_get_indexer(self):315 target = RangeIndex(10)316 indexer = self.index.get_indexer(target)317 expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1], dtype=np.intp)318 tm.assert_numpy_array_equal(indexer, expected)319 def test_get_indexer_pad(self):320 target = RangeIndex(10)321 indexer = self.index.get_indexer(target, method="pad")322 expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype=np.intp)323 tm.assert_numpy_array_equal(indexer, expected)324 def test_get_indexer_backfill(self):325 target = RangeIndex(10)326 indexer = self.index.get_indexer(target, method="backfill")327 expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5], dtype=np.intp)328 tm.assert_numpy_array_equal(indexer, expected)329 def test_join_outer(self):330 # join with Int64Index331 other = Int64Index(np.arange(25, 14, -1))332 res, lidx, ridx = self.index.join(other, how="outer", return_indexers=True)333 noidx_res = self.index.join(other, how="outer")334 tm.assert_index_equal(res, noidx_res)335 eres = Int64Index(336 [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]337 )338 elidx = np.array(339 [0, 1, 2, 3, 4, 5, 6, 7, -1, 8, -1, 9, -1, -1, -1, -1, -1, -1, -1],340 dtype=np.intp,341 )342 eridx = np.array(343 [-1, -1, -1, -1, -1, -1, -1, -1, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0],344 dtype=np.intp,345 )346 assert isinstance(res, Int64Index)347 assert not isinstance(res, RangeIndex)348 tm.assert_index_equal(res, eres)349 tm.assert_numpy_array_equal(lidx, elidx)350 tm.assert_numpy_array_equal(ridx, eridx)351 # join with RangeIndex352 other = RangeIndex(25, 14, -1)353 res, lidx, ridx = self.index.join(other, how="outer", return_indexers=True)354 noidx_res = self.index.join(other, how="outer")355 tm.assert_index_equal(res, noidx_res)356 assert isinstance(res, Int64Index)357 assert not isinstance(res, RangeIndex)358 tm.assert_index_equal(res, eres)359 tm.assert_numpy_array_equal(lidx, elidx)360 tm.assert_numpy_array_equal(ridx, eridx)361 def test_join_inner(self):362 # Join with non-RangeIndex363 other = Int64Index(np.arange(25, 14, -1))364 res, lidx, ridx = self.index.join(other, how="inner", return_indexers=True)365 # no guarantee of sortedness, so sort for comparison purposes366 ind = res.argsort()367 res = res.take(ind)368 lidx = lidx.take(ind)369 ridx = ridx.take(ind)370 eres = Int64Index([16, 18])371 elidx = np.array([8, 9], dtype=np.intp)372 eridx = np.array([9, 7], dtype=np.intp)373 assert isinstance(res, Int64Index)374 tm.assert_index_equal(res, eres)375 tm.assert_numpy_array_equal(lidx, elidx)376 tm.assert_numpy_array_equal(ridx, eridx)377 # Join two RangeIndex378 other = RangeIndex(25, 14, -1)379 res, lidx, ridx = self.index.join(other, how="inner", return_indexers=True)380 assert isinstance(res, RangeIndex)381 tm.assert_index_equal(res, eres)382 tm.assert_numpy_array_equal(lidx, elidx)383 tm.assert_numpy_array_equal(ridx, eridx)384 def test_join_left(self):385 # Join with Int64Index386 other = Int64Index(np.arange(25, 14, -1))387 res, lidx, ridx = self.index.join(other, how="left", return_indexers=True)388 eres = self.index389 eridx = np.array([-1, -1, -1, -1, -1, -1, -1, -1, 9, 7], dtype=np.intp)390 assert isinstance(res, RangeIndex)391 tm.assert_index_equal(res, eres)392 assert lidx is None393 tm.assert_numpy_array_equal(ridx, eridx)394 # Join withRangeIndex395 other = Int64Index(np.arange(25, 14, -1))396 res, lidx, ridx = self.index.join(other, how="left", return_indexers=True)397 assert isinstance(res, RangeIndex)398 tm.assert_index_equal(res, eres)399 assert lidx is None400 tm.assert_numpy_array_equal(ridx, eridx)401 def test_join_right(self):402 # Join with Int64Index403 other = Int64Index(np.arange(25, 14, -1))404 res, lidx, ridx = self.index.join(other, how="right", return_indexers=True)405 eres = other406 elidx = np.array([-1, -1, -1, -1, -1, -1, -1, 9, -1, 8, -1], dtype=np.intp)407 assert isinstance(other, Int64Index)408 tm.assert_index_equal(res, eres)409 tm.assert_numpy_array_equal(lidx, elidx)410 assert ridx is None411 # Join withRangeIndex412 other = RangeIndex(25, 14, -1)413 res, lidx, ridx = self.index.join(other, how="right", return_indexers=True)414 eres = other415 assert isinstance(other, RangeIndex)416 tm.assert_index_equal(res, eres)417 tm.assert_numpy_array_equal(lidx, elidx)418 assert ridx is None419 def test_join_non_int_index(self):420 other = Index([3, 6, 7, 8, 10], dtype=object)421 outer = self.index.join(other, how="outer")422 outer2 = other.join(self.index, how="outer")423 expected = Index([0, 2, 3, 4, 6, 7, 8, 10, 12, 14, 16, 18])424 tm.assert_index_equal(outer, outer2)425 tm.assert_index_equal(outer, expected)426 inner = self.index.join(other, how="inner")427 inner2 = other.join(self.index, how="inner")428 expected = Index([6, 8, 10])429 tm.assert_index_equal(inner, inner2)430 tm.assert_index_equal(inner, expected)431 left = self.index.join(other, how="left")432 tm.assert_index_equal(left, self.index.astype(object))433 left2 = other.join(self.index, how="left")...

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...38 with pytest.raises(TypeError, match=msg):39 self._holder()40 def test_to_series(self):41 # assert that we are creating a copy of the index42 idx = self.create_index()43 s = idx.to_series()44 assert s.values is not idx.values45 assert s.index is not idx46 assert s.name == idx.name47 def test_to_series_with_arguments(self):48 # GH1869949 # index kwarg50 idx = self.create_index()51 s = idx.to_series(index=idx)52 assert s.values is not idx.values53 assert s.index is idx54 assert s.name == idx.name55 # name kwarg56 idx = self.create_index()57 s = idx.to_series(name="__test")58 assert s.values is not idx.values59 assert s.index is not idx60 assert s.name != idx.name61 @pytest.mark.parametrize("name", [None, "new_name"])62 def test_to_frame(self, name):63 # see GH-15230, GH-2258064 idx = self.create_index()65 if name:66 idx_name = name67 else:68 idx_name = idx.name or 069 df = idx.to_frame(name=idx_name)70 assert df.index is idx71 assert len(df.columns) == 172 assert df.columns[0] == idx_name73 assert df[idx_name].values is not idx.values74 df = idx.to_frame(index=False, name=idx_name)75 assert df.index is not idx76 def test_to_frame_datetime_tz(self):77 # GH 2580978 idx = pd.date_range(start="2019-01-01", end="2019-01-30", freq="D")79 idx = idx.tz_localize("UTC")80 result = idx.to_frame()81 expected = pd.DataFrame(idx, index=idx)82 tm.assert_frame_equal(result, expected)83 def test_shift(self):84 # GH8083 test the base class for shift85 idx = self.create_index()86 msg = "Not supported for type {}".format(type(idx).__name__)87 with pytest.raises(NotImplementedError, match=msg):88 idx.shift(1)89 with pytest.raises(NotImplementedError, match=msg):90 idx.shift(1, 2)91 def test_create_index_existing_name(self):92 # GH11193, when an existing index is passed, and a new name is not93 # specified, the new index should inherit the previous object name94 expected = self.create_index()95 if not isinstance(expected, MultiIndex):96 expected.name = "foo"97 result = pd.Index(expected)98 tm.assert_index_equal(result, expected)99 result = pd.Index(expected, name="bar")100 expected.name = "bar"101 tm.assert_index_equal(result, expected)102 else:103 expected.names = ["foo", "bar"]104 result = pd.Index(expected)105 tm.assert_index_equal(106 result,107 Index(108 Index(109 [110 ("foo", "one"),111 ("foo", "two"),112 ("bar", "one"),113 ("baz", "two"),114 ("qux", "one"),115 ("qux", "two"),116 ],117 dtype="object",118 ),119 names=["foo", "bar"],120 ),121 )122 result = pd.Index(expected, names=["A", "B"])123 tm.assert_index_equal(124 result,125 Index(126 Index(127 [128 ("foo", "one"),129 ("foo", "two"),130 ("bar", "one"),131 ("baz", "two"),132 ("qux", "one"),133 ("qux", "two"),134 ],135 dtype="object",136 ),137 names=["A", "B"],138 ),139 )140 def test_numeric_compat(self):141 idx = self.create_index()142 with pytest.raises(TypeError, match="cannot perform __mul__"):143 idx * 1144 with pytest.raises(TypeError, match="cannot perform __rmul__"):145 1 * idx146 div_err = "cannot perform __truediv__"147 with pytest.raises(TypeError, match=div_err):148 idx / 1149 div_err = div_err.replace(" __", " __r")150 with pytest.raises(TypeError, match=div_err):151 1 / idx152 with pytest.raises(TypeError, match="cannot perform __floordiv__"):153 idx // 1154 with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):155 1 // idx156 def test_logical_compat(self):157 idx = self.create_index()158 with pytest.raises(TypeError, match="cannot perform all"):159 idx.all()160 with pytest.raises(TypeError, match="cannot perform any"):161 idx.any()162 def test_boolean_context_compat(self):163 # boolean context compat164 idx = self.create_index()165 with pytest.raises(ValueError, match="The truth value of a"):166 if idx:167 pass168 def test_reindex_base(self):169 idx = self.create_index()170 expected = np.arange(idx.size, dtype=np.intp)171 actual = idx.get_indexer(idx)172 tm.assert_numpy_array_equal(expected, actual)173 with pytest.raises(ValueError, match="Invalid fill method"):174 idx.get_indexer(idx, method="invalid")175 def test_get_indexer_consistency(self):176 # See GH 16819177 for name, index in self.indices.items():178 if isinstance(index, IntervalIndex):179 continue180 if index.is_unique or isinstance(index, CategoricalIndex):181 indexer = index.get_indexer(index[0:2])182 assert isinstance(indexer, np.ndarray)183 assert indexer.dtype == np.intp184 else:185 e = "Reindexing only valid with uniquely valued Index objects"186 with pytest.raises(InvalidIndexError, match=e):187 index.get_indexer(index[0:2])188 indexer, _ = index.get_indexer_non_unique(index[0:2])189 assert isinstance(indexer, np.ndarray)190 assert indexer.dtype == np.intp191 def test_ndarray_compat_properties(self):192 idx = self.create_index()193 assert idx.T.equals(idx)194 assert idx.transpose().equals(idx)195 values = idx.values196 for prop in self._compat_props:197 assert getattr(idx, prop) == getattr(values, prop)198 # test for validity199 idx.nbytes200 idx.values.nbytes201 def test_repr_roundtrip(self):202 idx = self.create_index()203 tm.assert_index_equal(eval(repr(idx)), idx)204 def test_str(self):205 # test the string repr206 idx = self.create_index()207 idx.name = "foo"208 assert "'foo'" in str(idx)209 assert idx.__class__.__name__ in str(idx)210 def test_repr_max_seq_item_setting(self):211 # GH10182212 idx = self.create_index()213 idx = idx.repeat(50)214 with pd.option_context("display.max_seq_items", None):215 repr(idx)216 assert "..." not in str(idx)217 def test_copy_name(self):218 # gh-12309: Check that the "name" argument219 # passed at initialization is honored.220 for name, index in self.indices.items():221 if isinstance(index, MultiIndex):222 continue223 first = index.__class__(index, copy=True, name="mario")224 second = first.__class__(first, copy=False)225 # Even though "copy=False", we want a new object.226 assert first is not second227 # Not using tm.assert_index_equal() since names differ.228 assert index.equals(first)229 assert first.name == "mario"230 assert second.name == "mario"231 s1 = Series(2, index=first)232 s2 = Series(3, index=second[:-1])233 if not isinstance(index, CategoricalIndex):234 # See gh-13365235 s3 = s1 * s2236 assert s3.index.name == "mario"237 def test_ensure_copied_data(self):238 # Check the "copy" argument of each Index.__new__ is honoured239 # GH12309240 for name, index in self.indices.items():241 init_kwargs = {}242 if isinstance(index, PeriodIndex):243 # Needs "freq" specification:244 init_kwargs["freq"] = index.freq245 elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):246 # RangeIndex cannot be initialized from data247 # MultiIndex and CategoricalIndex are tested separately248 continue249 index_type = index.__class__250 result = index_type(index.values, copy=True, **init_kwargs)251 tm.assert_index_equal(index, result)252 tm.assert_numpy_array_equal(253 index._ndarray_values, result._ndarray_values, check_same="copy"254 )255 if isinstance(index, PeriodIndex):256 # .values an object array of Period, thus copied257 result = index_type(ordinal=index.asi8, copy=False, **init_kwargs)258 tm.assert_numpy_array_equal(259 index._ndarray_values, result._ndarray_values, check_same="same"260 )261 elif isinstance(index, IntervalIndex):262 # checked in test_interval.py263 pass264 else:265 result = index_type(index.values, copy=False, **init_kwargs)266 tm.assert_numpy_array_equal(267 index.values, result.values, check_same="same"268 )269 tm.assert_numpy_array_equal(270 index._ndarray_values, result._ndarray_values, check_same="same"271 )272 def test_memory_usage(self):273 for name, index in self.indices.items():274 result = index.memory_usage()275 if len(index):276 index.get_loc(index[0])277 result2 = index.memory_usage()278 result3 = index.memory_usage(deep=True)279 # RangeIndex, IntervalIndex280 # don't have engines281 if not isinstance(index, (RangeIndex, IntervalIndex)):282 assert result2 > result283 if index.inferred_type == "object":284 assert result3 > result2285 else:286 # we report 0 for no-length287 assert result == 0288 def test_argsort(self):289 for k, ind in self.indices.items():290 # separately tested291 if k in ["catIndex"]:292 continue293 result = ind.argsort()294 expected = np.array(ind).argsort()295 tm.assert_numpy_array_equal(result, expected, check_dtype=False)296 def test_numpy_argsort(self):297 for k, ind in self.indices.items():298 result = np.argsort(ind)299 expected = ind.argsort()300 tm.assert_numpy_array_equal(result, expected)301 # these are the only two types that perform302 # pandas compatibility input validation - the303 # rest already perform separate (or no) such304 # validation via their 'values' attribute as305 # defined in pandas.core.indexes/base.py - they306 # cannot be changed at the moment due to307 # backwards compatibility concerns308 if isinstance(type(ind), (CategoricalIndex, RangeIndex)):309 msg = "the 'axis' parameter is not supported"310 with pytest.raises(ValueError, match=msg):311 np.argsort(ind, axis=1)312 msg = "the 'kind' parameter is not supported"313 with pytest.raises(ValueError, match=msg):314 np.argsort(ind, kind="mergesort")315 msg = "the 'order' parameter is not supported"316 with pytest.raises(ValueError, match=msg):317 np.argsort(ind, order=("a", "b"))318 def test_take(self):319 indexer = [4, 3, 0, 2]320 for k, ind in self.indices.items():321 # separate322 if k in ["boolIndex", "tuples", "empty"]:323 continue324 result = ind.take(indexer)325 expected = ind[indexer]326 assert result.equals(expected)327 if not isinstance(ind, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):328 # GH 10791329 with pytest.raises(AttributeError):330 ind.freq331 def test_take_invalid_kwargs(self):332 idx = self.create_index()333 indices = [1, 2]334 msg = r"take\(\) got an unexpected keyword argument 'foo'"335 with pytest.raises(TypeError, match=msg):336 idx.take(indices, foo=2)337 msg = "the 'out' parameter is not supported"338 with pytest.raises(ValueError, match=msg):339 idx.take(indices, out=indices)340 msg = "the 'mode' parameter is not supported"341 with pytest.raises(ValueError, match=msg):342 idx.take(indices, mode="clip")343 def test_repeat(self):344 rep = 2345 i = self.create_index()346 expected = pd.Index(i.values.repeat(rep), name=i.name)347 tm.assert_index_equal(i.repeat(rep), expected)348 i = self.create_index()349 rep = np.arange(len(i))350 expected = pd.Index(i.values.repeat(rep), name=i.name)351 tm.assert_index_equal(i.repeat(rep), expected)352 def test_numpy_repeat(self):353 rep = 2354 i = self.create_index()355 expected = i.repeat(rep)356 tm.assert_index_equal(np.repeat(i, rep), expected)357 msg = "the 'axis' parameter is not supported"358 with pytest.raises(ValueError, match=msg):359 np.repeat(i, rep, axis=0)360 @pytest.mark.parametrize("klass", [list, tuple, np.array, Series])361 def test_where(self, klass):362 i = self.create_index()363 cond = [True] * len(i)364 result = i.where(klass(cond))365 expected = i366 tm.assert_index_equal(result, expected)367 cond = [False] + [True] * len(i[1:])368 expected = pd.Index([i._na_value] + i[1:].tolist(), dtype=i.dtype)369 result = i.where(klass(cond))370 tm.assert_index_equal(result, expected)371 @pytest.mark.parametrize("case", [0.5, "xxx"])372 @pytest.mark.parametrize(373 "method", ["intersection", "union", "difference", "symmetric_difference"]374 )375 def test_set_ops_error_cases(self, case, method):376 for name, idx in self.indices.items():377 # non-iterable input378 msg = "Input must be Index or array-like"379 with pytest.raises(TypeError, match=msg):380 getattr(idx, method)(case)381 def test_intersection_base(self):382 for name, idx in self.indices.items():383 first = idx[:5]384 second = idx[:3]385 intersect = first.intersection(second)386 if isinstance(idx, CategoricalIndex):387 pass388 else:389 assert tm.equalContents(intersect, second)390 # GH 10149391 cases = [klass(second.values) for klass in [np.array, Series, list]]392 for case in cases:393 if isinstance(idx, CategoricalIndex):394 pass395 else:396 result = first.intersection(case)397 assert tm.equalContents(result, second)398 if isinstance(idx, MultiIndex):399 msg = "other must be a MultiIndex or a list of tuples"400 with pytest.raises(TypeError, match=msg):401 first.intersection([1, 2, 3])402 def test_union_base(self):403 for name, idx in self.indices.items():404 first = idx[3:]405 second = idx[:5]406 everything = idx407 union = first.union(second)408 assert tm.equalContents(union, everything)409 # GH 10149410 cases = [klass(second.values) for klass in [np.array, Series, list]]411 for case in cases:412 if isinstance(idx, CategoricalIndex):413 pass414 else:415 result = first.union(case)416 assert tm.equalContents(result, everything)417 if isinstance(idx, MultiIndex):418 msg = "other must be a MultiIndex or a list of tuples"419 with pytest.raises(TypeError, match=msg):420 first.union([1, 2, 3])421 @pytest.mark.parametrize("sort", [None, False])422 def test_difference_base(self, sort):423 for name, idx in self.indices.items():424 first = idx[2:]425 second = idx[:4]426 answer = idx[4:]427 result = first.difference(second, sort)428 if isinstance(idx, CategoricalIndex):429 pass430 else:431 assert tm.equalContents(result, answer)432 # GH 10149433 cases = [klass(second.values) for klass in [np.array, Series, list]]434 for case in cases:435 if isinstance(idx, CategoricalIndex):436 pass437 elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):438 assert result.__class__ == answer.__class__439 tm.assert_numpy_array_equal(440 result.sort_values().asi8, answer.sort_values().asi8441 )442 else:443 result = first.difference(case, sort)444 assert tm.equalContents(result, answer)445 if isinstance(idx, MultiIndex):446 msg = "other must be a MultiIndex or a list of tuples"447 with pytest.raises(TypeError, match=msg):448 first.difference([1, 2, 3], sort)449 def test_symmetric_difference(self):450 for name, idx in self.indices.items():451 first = idx[1:]452 second = idx[:-1]453 if isinstance(idx, CategoricalIndex):454 pass455 else:456 answer = idx[[0, -1]]457 result = first.symmetric_difference(second)458 assert tm.equalContents(result, answer)459 # GH 10149460 cases = [klass(second.values) for klass in [np.array, Series, list]]461 for case in cases:462 if isinstance(idx, CategoricalIndex):463 pass464 else:465 result = first.symmetric_difference(case)466 assert tm.equalContents(result, answer)467 if isinstance(idx, MultiIndex):468 msg = "other must be a MultiIndex or a list of tuples"469 with pytest.raises(TypeError, match=msg):470 first.symmetric_difference([1, 2, 3])471 def test_insert_base(self):472 for name, idx in self.indices.items():473 result = idx[1:4]474 if not len(idx):475 continue476 # test 0th element477 assert idx[0:4].equals(result.insert(0, idx[0]))478 def test_delete_base(self):479 for name, idx in self.indices.items():480 if not len(idx):481 continue482 if isinstance(idx, RangeIndex):483 # tested in class484 continue485 expected = idx[1:]486 result = idx.delete(0)487 assert result.equals(expected)488 assert result.name == expected.name489 expected = idx[:-1]490 result = idx.delete(-1)491 assert result.equals(expected)492 assert result.name == expected.name493 with pytest.raises((IndexError, ValueError)):494 # either depending on numpy version495 idx.delete(len(idx))496 def test_equals(self):497 for name, idx in self.indices.items():498 assert idx.equals(idx)499 assert idx.equals(idx.copy())500 assert idx.equals(idx.astype(object))501 assert not idx.equals(list(idx))502 assert not idx.equals(np.array(idx))503 # Cannot pass in non-int64 dtype to RangeIndex504 if not isinstance(idx, RangeIndex):505 same_values = Index(idx, dtype=object)506 assert idx.equals(same_values)507 assert same_values.equals(idx)508 if idx.nlevels == 1:509 # do not test MultiIndex510 assert not idx.equals(pd.Series(idx))511 def test_equals_op(self):512 # GH9947, GH10637513 index_a = self.create_index()514 if isinstance(index_a, PeriodIndex):515 pytest.skip("Skip check for PeriodIndex")516 n = len(index_a)517 index_b = index_a[0:-1]518 index_c = index_a[0:-1].append(index_a[-2:-1])519 index_d = index_a[0:1]520 msg = "Lengths must match|could not be broadcast"521 with pytest.raises(ValueError, match=msg):522 index_a == index_b523 expected1 = np.array([True] * n)524 expected2 = np.array([True] * (n - 1) + [False])525 tm.assert_numpy_array_equal(index_a == index_a, expected1)526 tm.assert_numpy_array_equal(index_a == index_c, expected2)527 # test comparisons with numpy arrays528 array_a = np.array(index_a)529 array_b = np.array(index_a[0:-1])530 array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))531 array_d = np.array(index_a[0:1])532 with pytest.raises(ValueError, match=msg):533 index_a == array_b534 tm.assert_numpy_array_equal(index_a == array_a, expected1)535 tm.assert_numpy_array_equal(index_a == array_c, expected2)536 # test comparisons with Series537 series_a = Series(array_a)538 series_b = Series(array_b)539 series_c = Series(array_c)540 series_d = Series(array_d)541 with pytest.raises(ValueError, match=msg):542 index_a == series_b543 tm.assert_numpy_array_equal(index_a == series_a, expected1)544 tm.assert_numpy_array_equal(index_a == series_c, expected2)545 # cases where length is 1 for one of them546 with pytest.raises(ValueError, match="Lengths must match"):547 index_a == index_d548 with pytest.raises(ValueError, match="Lengths must match"):549 index_a == series_d550 with pytest.raises(ValueError, match="Lengths must match"):551 index_a == array_d552 msg = "Can only compare identically-labeled Series objects"553 with pytest.raises(ValueError, match=msg):554 series_a == series_d555 with pytest.raises(ValueError, match="Lengths must match"):556 series_a == array_d557 # comparing with a scalar should broadcast; note that we are excluding558 # MultiIndex because in this case each item in the index is a tuple of559 # length 2, and therefore is considered an array of length 2 in the560 # comparison instead of a scalar561 if not isinstance(index_a, MultiIndex):562 expected3 = np.array([False] * (len(index_a) - 2) + [True, False])563 # assuming the 2nd to last item is unique in the data564 item = index_a[-2]565 tm.assert_numpy_array_equal(index_a == item, expected3)566 tm.assert_series_equal(series_a == item, Series(expected3))567 def test_hasnans_isnans(self):568 # GH 11343, added tests for hasnans / isnans569 for name, index in self.indices.items():570 if isinstance(index, MultiIndex):571 pass572 else:573 idx = index.copy()574 # cases in indices doesn't include NaN575 expected = np.array([False] * len(idx), dtype=bool)576 tm.assert_numpy_array_equal(idx._isnan, expected)577 assert idx.hasnans is False578 idx = index.copy()579 values = np.asarray(idx.values)580 if len(index) == 0:581 continue582 elif isinstance(index, DatetimeIndexOpsMixin):583 values[1] = iNaT584 elif isinstance(index, (Int64Index, UInt64Index)):585 continue586 else:587 values[1] = np.nan588 if isinstance(index, PeriodIndex):589 idx = index.__class__(values, freq=index.freq)590 else:591 idx = index.__class__(values)592 expected = np.array([False] * len(idx), dtype=bool)593 expected[1] = True594 tm.assert_numpy_array_equal(idx._isnan, expected)595 assert idx.hasnans is True596 def test_fillna(self):597 # GH 11343598 for name, index in self.indices.items():599 if len(index) == 0:600 pass601 elif isinstance(index, MultiIndex):602 idx = index.copy()603 msg = "isna is not defined for MultiIndex"604 with pytest.raises(NotImplementedError, match=msg):605 idx.fillna(idx[0])606 else:607 idx = index.copy()608 result = idx.fillna(idx[0])609 tm.assert_index_equal(result, idx)610 assert result is not idx611 msg = "'value' must be a scalar, passed: "612 with pytest.raises(TypeError, match=msg):613 idx.fillna([idx[0]])614 idx = index.copy()615 values = np.asarray(idx.values)616 if isinstance(index, DatetimeIndexOpsMixin):617 values[1] = iNaT618 elif isinstance(index, (Int64Index, UInt64Index)):619 continue620 else:621 values[1] = np.nan622 if isinstance(index, PeriodIndex):623 idx = index.__class__(values, freq=index.freq)624 else:625 idx = index.__class__(values)626 expected = np.array([False] * len(idx), dtype=bool)627 expected[1] = True628 tm.assert_numpy_array_equal(idx._isnan, expected)629 assert idx.hasnans is True630 def test_nulls(self):631 # this is really a smoke test for the methods632 # as these are adequately tested for function elsewhere633 for name, index in self.indices.items():634 if len(index) == 0:635 tm.assert_numpy_array_equal(index.isna(), np.array([], dtype=bool))636 elif isinstance(index, MultiIndex):637 idx = index.copy()638 msg = "isna is not defined for MultiIndex"639 with pytest.raises(NotImplementedError, match=msg):640 idx.isna()641 else:642 if not index.hasnans:643 tm.assert_numpy_array_equal(644 index.isna(), np.zeros(len(index), dtype=bool)645 )646 tm.assert_numpy_array_equal(647 index.notna(), np.ones(len(index), dtype=bool)648 )649 else:650 result = isna(index)651 tm.assert_numpy_array_equal(index.isna(), result)652 tm.assert_numpy_array_equal(index.notna(), ~result)653 def test_empty(self):654 # GH 15270655 index = self.create_index()656 assert not index.empty657 assert index[:0].empty658 def test_join_self_unique(self, join_type):659 index = self.create_index()660 if index.is_unique:661 joined = index.join(index, how=join_type)662 assert (index == joined).all()663 def test_map(self):664 # callable665 index = self.create_index()666 # we don't infer UInt64667 if isinstance(index, pd.UInt64Index):668 expected = index.astype("int64")669 else:670 expected = index671 result = index.map(lambda x: x)672 tm.assert_index_equal(result, expected)673 @pytest.mark.parametrize(674 "mapper",675 [676 lambda values, index: {i: e for e, i in zip(values, index)},677 lambda values, index: pd.Series(values, index),678 ],679 )680 def test_map_dictlike(self, mapper):681 index = self.create_index()682 if isinstance(index, (pd.CategoricalIndex, pd.IntervalIndex)):683 pytest.skip("skipping tests for {}".format(type(index)))684 identity = mapper(index.values, index)685 # we don't infer to UInt64 for a dict686 if isinstance(index, pd.UInt64Index) and isinstance(identity, dict):687 expected = index.astype("int64")688 else:689 expected = index690 result = index.map(identity)691 tm.assert_index_equal(result, expected)692 # empty mappable693 expected = pd.Index([np.nan] * len(index))694 result = index.map(mapper(expected, index))695 tm.assert_index_equal(result, expected)696 def test_putmask_with_wrong_mask(self):697 # GH18368698 index = self.create_index()699 with pytest.raises(ValueError):700 index.putmask(np.ones(len(index) + 1, np.bool), 1)701 with pytest.raises(ValueError):702 index.putmask(np.ones(len(index) - 1, np.bool), 1)703 with pytest.raises(ValueError):704 index.putmask("foo", 1)705 @pytest.mark.parametrize("copy", [True, False])706 @pytest.mark.parametrize("name", [None, "foo"])707 @pytest.mark.parametrize("ordered", [True, False])708 def test_astype_category(self, copy, name, ordered):709 # GH 18630710 index = self.create_index()711 if name:712 index = index.rename(name)713 # standard categories714 dtype = CategoricalDtype(ordered=ordered)715 result = index.astype(dtype, copy=copy)716 expected = CategoricalIndex(index.values, name=name, ordered=ordered)717 tm.assert_index_equal(result, expected)718 # non-standard categories719 dtype = CategoricalDtype(index.unique().tolist()[:-1], ordered)720 result = index.astype(dtype, copy=copy)721 expected = CategoricalIndex(index.values, name=name, dtype=dtype)722 tm.assert_index_equal(result, expected)723 if ordered is False:724 # dtype='category' defaults to ordered=False, so only test once725 result = index.astype("category", copy=copy)726 expected = CategoricalIndex(index.values, name=name)727 tm.assert_index_equal(result, expected)728 def test_is_unique(self):729 # initialize a unique index730 index = self.create_index().drop_duplicates()731 assert index.is_unique is True732 # empty index should be unique733 index_empty = index[:0]734 assert index_empty.is_unique is True735 # test basic dupes736 index_dup = index.insert(0, index[0])737 assert index_dup.is_unique is False738 # single NA should be unique739 index_na = index.insert(0, np.nan)740 assert index_na.is_unique is True741 # multiple NA should not be unique742 index_na_dup = index_na.insert(0, np.nan)...

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conway.py

Source:conway.py Github

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...232 y = 0233 checkForRule = lines[valueLineNumber].find(", rule")234 if checkForRule == -1:235 #no rule 236 commaIndex = lines[valueLineNumber].index(',')237 x = int(lines[valueLineNumber][4:commaIndex])238 y = int(lines[valueLineNumber][commaIndex + 5:])239 else:240 subs = lines[valueLineNumber][0:checkForRule]241 commaIndex = subs.index(',')242 x = int(subs[4:commaIndex])243 y = int(subs[commaIndex + 5:])244 if x >= self.rows or y >= self.cols:245 raise Exception("Pattern to big to fit in grid")246 if x + position[0] >= self.cols or y + position[1] >= self.rows:247 raise Exception("Pattern unable to fit pattern at that position")248 joinedLines = "".join(lines[valueLineNumber+1:]).split("$")249 i = 0250 while i < y:251 j = 0252 xindex = 0253 while j < len(joinedLines[i]):254 #has run_counter255 run_counter = 1...

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test_setops.py

Source:test_setops.py Github

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...7 return request.param8@pytest.fixture(params=[None, False])9def sort(request):10 return request.param11def monotonic_index(start, end, dtype="int64", closed="right"):12 return IntervalIndex.from_breaks(np.arange(start, end, dtype=dtype), closed=closed)13def empty_index(dtype="int64", closed="right"):14 return IntervalIndex(np.array([], dtype=dtype), closed=closed)15class TestIntervalIndex:16 def test_union(self, closed, sort):17 index = monotonic_index(0, 11, closed=closed)18 other = monotonic_index(5, 13, closed=closed)19 expected = monotonic_index(0, 13, closed=closed)20 result = index[::-1].union(other, sort=sort)21 if sort is None:22 tm.assert_index_equal(result, expected)23 assert tm.equalContents(result, expected)24 result = other[::-1].union(index, sort=sort)25 if sort is None:26 tm.assert_index_equal(result, expected)27 assert tm.equalContents(result, expected)28 tm.assert_index_equal(index.union(index, sort=sort), index)29 tm.assert_index_equal(index.union(index[:1], sort=sort), index)30 # GH 19101: empty result, same dtype31 index = empty_index(dtype="int64", closed=closed)32 result = index.union(index, sort=sort)33 tm.assert_index_equal(result, index)34 # GH 19101: empty result, different dtypes35 other = empty_index(dtype="float64", closed=closed)36 result = index.union(other, sort=sort)37 tm.assert_index_equal(result, index)38 def test_intersection(self, closed, sort):39 index = monotonic_index(0, 11, closed=closed)40 other = monotonic_index(5, 13, closed=closed)41 expected = monotonic_index(5, 11, closed=closed)42 result = index[::-1].intersection(other, sort=sort)43 if sort is None:44 tm.assert_index_equal(result, expected)45 assert tm.equalContents(result, expected)46 result = other[::-1].intersection(index, sort=sort)47 if sort is None:48 tm.assert_index_equal(result, expected)49 assert tm.equalContents(result, expected)50 tm.assert_index_equal(index.intersection(index, sort=sort), index)51 # GH 19101: empty result, same dtype52 other = monotonic_index(300, 314, closed=closed)53 expected = empty_index(dtype="int64", closed=closed)54 result = index.intersection(other, sort=sort)55 tm.assert_index_equal(result, expected)56 # GH 19101: empty result, different dtypes57 other = monotonic_index(300, 314, dtype="float64", closed=closed)58 result = index.intersection(other, sort=sort)59 tm.assert_index_equal(result, expected)60 # GH 26225: nested intervals61 index = IntervalIndex.from_tuples([(1, 2), (1, 3), (1, 4), (0, 2)])62 other = IntervalIndex.from_tuples([(1, 2), (1, 3)])63 expected = IntervalIndex.from_tuples([(1, 2), (1, 3)])64 result = index.intersection(other)65 tm.assert_index_equal(result, expected)66 # GH 26225: duplicate element67 index = IntervalIndex.from_tuples([(1, 2), (1, 2), (2, 3), (3, 4)])68 other = IntervalIndex.from_tuples([(1, 2), (2, 3)])69 expected = IntervalIndex.from_tuples([(1, 2), (1, 2), (2, 3)])70 result = index.intersection(other)71 tm.assert_index_equal(result, expected)72 # GH 2622573 index = IntervalIndex.from_tuples([(0, 3), (0, 2)])74 other = IntervalIndex.from_tuples([(0, 2), (1, 3)])75 expected = IntervalIndex.from_tuples([(0, 2)])76 result = index.intersection(other)77 tm.assert_index_equal(result, expected)78 # GH 26225: duplicate nan element79 index = IntervalIndex([np.nan, np.nan])80 other = IntervalIndex([np.nan])81 expected = IntervalIndex([np.nan])82 result = index.intersection(other)83 tm.assert_index_equal(result, expected)84 def test_difference(self, closed, sort):85 index = IntervalIndex.from_arrays([1, 0, 3, 2], [1, 2, 3, 4], closed=closed)86 result = index.difference(index[:1], sort=sort)87 expected = index[1:]88 if sort is None:89 expected = expected.sort_values()90 tm.assert_index_equal(result, expected)91 # GH 19101: empty result, same dtype92 result = index.difference(index, sort=sort)93 expected = empty_index(dtype="int64", closed=closed)94 tm.assert_index_equal(result, expected)95 # GH 19101: empty result, different dtypes96 other = IntervalIndex.from_arrays(97 index.left.astype("float64"), index.right, closed=closed98 )99 result = index.difference(other, sort=sort)100 tm.assert_index_equal(result, expected)101 def test_symmetric_difference(self, closed, sort):102 index = monotonic_index(0, 11, closed=closed)103 result = index[1:].symmetric_difference(index[:-1], sort=sort)104 expected = IntervalIndex([index[0], index[-1]])105 if sort is None:106 tm.assert_index_equal(result, expected)107 assert tm.equalContents(result, expected)108 # GH 19101: empty result, same dtype109 result = index.symmetric_difference(index, sort=sort)110 expected = empty_index(dtype="int64", closed=closed)111 if sort is None:112 tm.assert_index_equal(result, expected)113 assert tm.equalContents(result, expected)114 # GH 19101: empty result, different dtypes115 other = IntervalIndex.from_arrays(116 index.left.astype("float64"), index.right, closed=closed117 )118 result = index.symmetric_difference(other, sort=sort)119 tm.assert_index_equal(result, expected)120 @pytest.mark.parametrize(121 "op_name", ["union", "intersection", "difference", "symmetric_difference"]122 )123 @pytest.mark.parametrize("sort", [None, False])124 def test_set_incompatible_types(self, closed, op_name, sort):125 index = monotonic_index(0, 11, closed=closed)126 set_op = getattr(index, op_name)127 # TODO: standardize return type of non-union setops type(self vs other)128 # non-IntervalIndex129 if op_name == "difference":130 expected = index131 else:132 expected = getattr(index.astype("O"), op_name)(Index([1, 2, 3]))133 result = set_op(Index([1, 2, 3]), sort=sort)134 tm.assert_index_equal(result, expected)135 # mixed closed136 msg = (137 "can only do set operations between two IntervalIndex objects "138 "that are closed on the same side"139 )140 for other_closed in {"right", "left", "both", "neither"} - {closed}:141 other = monotonic_index(0, 11, closed=other_closed)142 with pytest.raises(ValueError, match=msg):143 set_op(other, sort=sort)144 # GH 19016: incompatible dtypes145 other = interval_range(Timestamp("20180101"), periods=9, closed=closed)146 msg = (147 "can only do {op} between two IntervalIndex objects that have "148 "compatible dtypes"149 ).format(op=op_name)150 with pytest.raises(TypeError, match=msg):...

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