import numpy as np import pytest from pandas.core.dtypes.common import ensure_platform_int import pandas as pd from pandas import Float64Index, Index, Int64Index, RangeIndex import pandas._testing as tm from ..test_numeric import Numeric # aliases to make some tests easier to read RI = RangeIndex I64 = Int64Index F64 = Float64Index OI = Index class TestRangeIndex(Numeric): _holder = RangeIndex _compat_props = ["shape", "ndim", "size"] @pytest.fixture( params=[ RangeIndex(start=0, stop=20, step=2, name="foo"), RangeIndex(start=18, stop=-1, step=-2, name="bar"), ], ids=["index_inc", "index_dec"], ) def index(self, request): return request.param def create_index(self) -> RangeIndex: return RangeIndex(start=0, stop=20, step=2) def test_can_hold_identifiers(self): idx = self.create_index() key = idx[0] assert idx._can_hold_identifiers_and_holds_name(key) is False def test_too_many_names(self): index = self.create_index() with pytest.raises(ValueError, match="^Length"): index.names = ["roger", "harold"] @pytest.mark.parametrize( "index, start, stop, step", [ (RangeIndex(5), 0, 5, 1), (RangeIndex(0, 5), 0, 5, 1), (RangeIndex(5, step=2), 0, 5, 2), (RangeIndex(1, 5, 2), 1, 5, 2), ], ) def test_start_stop_step_attrs(self, index, start, stop, step): # GH 25710 assert index.start == start assert index.stop == stop assert index.step == step @pytest.mark.parametrize("attr_name", ["_start", "_stop", "_step"]) def test_deprecated_start_stop_step_attrs(self, attr_name): # GH 26581 idx = self.create_index() with tm.assert_produces_warning(FutureWarning): getattr(idx, attr_name) def test_copy(self): i = RangeIndex(5, name="Foo") i_copy = i.copy() assert i_copy is not i assert i_copy.identical(i) assert i_copy._range == range(0, 5, 1) assert i_copy.name == "Foo" def test_repr(self): i = RangeIndex(5, name="Foo") result = repr(i) expected = "RangeIndex(start=0, stop=5, step=1, name='Foo')" assert result == expected result = eval(result) tm.assert_index_equal(result, i, exact=True) i = RangeIndex(5, 0, -1) result = repr(i) expected = "RangeIndex(start=5, stop=0, step=-1)" assert result == expected result = eval(result) tm.assert_index_equal(result, i, exact=True) def test_insert(self): idx = RangeIndex(5, name="Foo") result = idx[1:4] # test 0th element tm.assert_index_equal(idx[0:4], result.insert(0, idx[0])) # GH 18295 (test missing) expected = Float64Index([0, np.nan, 1, 2, 3, 4]) for na in [np.nan, None, pd.NA]: result = RangeIndex(5).insert(1, na) tm.assert_index_equal(result, expected) result = RangeIndex(5).insert(1, pd.NaT) expected = Index([0, pd.NaT, 1, 2, 3, 4], dtype=object) tm.assert_index_equal(result, expected) def test_delete(self): idx = RangeIndex(5, name="Foo") expected = idx[1:].astype(int) result = idx.delete(0) tm.assert_index_equal(result, expected) assert result.name == expected.name expected = idx[:-1].astype(int) result = idx.delete(-1) tm.assert_index_equal(result, expected) assert result.name == expected.name msg = "index 5 is out of bounds for axis 0 with size 5" with pytest.raises((IndexError, ValueError), match=msg): # either depending on numpy version result = idx.delete(len(idx)) def test_view(self): i = RangeIndex(0, name="Foo") i_view = i.view() assert i_view.name == "Foo" i_view = i.view("i8") tm.assert_numpy_array_equal(i.values, i_view) i_view = i.view(RangeIndex) tm.assert_index_equal(i, i_view) def test_dtype(self): index = self.create_index() assert index.dtype == np.int64 def test_cache(self): # GH 26565, GH26617, GH35432 # This test checks whether _cache has been set. # Calling RangeIndex._cache["_data"] creates an int64 array of the same length # as the RangeIndex and stores it in _cache. idx = RangeIndex(0, 100, 10) assert idx._cache == {} repr(idx) assert idx._cache == {} str(idx) assert idx._cache == {} idx.get_loc(20) assert idx._cache == {} 90 in idx # True assert idx._cache == {} 91 in idx # False assert idx._cache == {} idx.all() assert idx._cache == {} idx.any() assert idx._cache == {} for _ in idx: pass assert idx._cache == {} idx.format() assert idx._cache == {} df = pd.DataFrame({"a": range(10)}, index=idx) str(df) assert idx._cache == {} df.loc[50] assert idx._cache == {} with pytest.raises(KeyError, match="51"): df.loc[51] assert idx._cache == {} df.loc[10:50] assert idx._cache == {} df.iloc[5:10] assert idx._cache == {} # idx._cache should contain a _data entry after call to idx._data idx._data assert isinstance(idx._data, np.ndarray) assert idx._data is idx._data # check cached value is reused assert len(idx._cache) == 4 expected = np.arange(0, 100, 10, dtype="int64") tm.assert_numpy_array_equal(idx._cache["_data"], expected) def test_is_monotonic(self): index = RangeIndex(0, 20, 2) assert index.is_monotonic is True assert index.is_monotonic_increasing is True assert index.is_monotonic_decreasing is False assert index._is_strictly_monotonic_increasing is True assert index._is_strictly_monotonic_decreasing is False index = RangeIndex(4, 0, -1) assert index.is_monotonic is False assert index._is_strictly_monotonic_increasing is False assert index.is_monotonic_decreasing is True assert index._is_strictly_monotonic_decreasing is True index = RangeIndex(1, 2) assert index.is_monotonic is True assert index.is_monotonic_increasing is True assert index.is_monotonic_decreasing is True assert index._is_strictly_monotonic_increasing is True assert index._is_strictly_monotonic_decreasing is True index = RangeIndex(2, 1) assert index.is_monotonic is True assert index.is_monotonic_increasing is True assert index.is_monotonic_decreasing is True assert index._is_strictly_monotonic_increasing is True assert index._is_strictly_monotonic_decreasing is True index = RangeIndex(1, 1) assert index.is_monotonic is True assert index.is_monotonic_increasing is True assert index.is_monotonic_decreasing is True assert index._is_strictly_monotonic_increasing is True assert index._is_strictly_monotonic_decreasing is True def test_equals_range(self): equiv_pairs = [ (RangeIndex(0, 9, 2), RangeIndex(0, 10, 2)), (RangeIndex(0), RangeIndex(1, -1, 3)), (RangeIndex(1, 2, 3), RangeIndex(1, 3, 4)), (RangeIndex(0, -9, -2), RangeIndex(0, -10, -2)), ] for left, right in equiv_pairs: assert left.equals(right) assert right.equals(left) def test_logical_compat(self): idx = self.create_index() assert idx.all() == idx.values.all() assert idx.any() == idx.values.any() def test_identical(self): index = self.create_index() i = Index(index.copy()) assert i.identical(index) # we don't allow object dtype for RangeIndex if isinstance(index, RangeIndex): return same_values_different_type = Index(i, dtype=object) assert not i.identical(same_values_different_type) i = index.copy(dtype=object) i = i.rename("foo") same_values = Index(i, dtype=object) assert same_values.identical(index.copy(dtype=object)) assert not i.identical(index) assert Index(same_values, name="foo", dtype=object).identical(i) assert not index.copy(dtype=object).identical(index.copy(dtype="int64")) def test_nbytes(self): # memory savings vs int index i = RangeIndex(0, 1000) assert i.nbytes < i._int64index.nbytes / 10 # constant memory usage i2 = RangeIndex(0, 10) assert i.nbytes == i2.nbytes @pytest.mark.parametrize( "start,stop,step", [ # can't ("foo", "bar", "baz"), # shouldn't ("0", "1", "2"), ], ) def test_cant_or_shouldnt_cast(self, start, stop, step): msg = f"Wrong type {type(start)} for value {start}" with pytest.raises(TypeError, match=msg): RangeIndex(start, stop, step) def test_view_index(self): index = self.create_index() index.view(Index) def test_prevent_casting(self): index = self.create_index() result = index.astype("O") assert result.dtype == np.object_ def test_repr_roundtrip(self): index = self.create_index() tm.assert_index_equal(eval(repr(index)), index) def test_slice_keep_name(self): idx = RangeIndex(1, 2, name="asdf") assert idx.name == idx[1:].name def test_has_duplicates(self, index): assert index.is_unique assert not index.has_duplicates def test_extended_gcd(self): index = self.create_index() result = index._extended_gcd(6, 10) assert result[0] == result[1] * 6 + result[2] * 10 assert 2 == result[0] result = index._extended_gcd(10, 6) assert 2 == result[1] * 10 + result[2] * 6 assert 2 == result[0] def test_min_fitting_element(self): result = RangeIndex(0, 20, 2)._min_fitting_element(1) assert 2 == result result = RangeIndex(1, 6)._min_fitting_element(1) assert 1 == result result = RangeIndex(18, -2, -2)._min_fitting_element(1) assert 2 == result result = RangeIndex(5, 0, -1)._min_fitting_element(1) assert 1 == result big_num = 500000000000000000000000 result = RangeIndex(5, big_num * 2, 1)._min_fitting_element(big_num) assert big_num == result def test_max_fitting_element(self): result = RangeIndex(0, 20, 2)._max_fitting_element(17) assert 16 == result result = RangeIndex(1, 6)._max_fitting_element(4) assert 4 == result result = RangeIndex(18, -2, -2)._max_fitting_element(17) assert 16 == result result = RangeIndex(5, 0, -1)._max_fitting_element(4) assert 4 == result big_num = 500000000000000000000000 result = RangeIndex(5, big_num * 2, 1)._max_fitting_element(big_num) assert big_num == result def test_pickle_compat_construction(self): # RangeIndex() is a valid constructor pass def test_slice_specialised(self): index = self.create_index() index.name = "foo" # scalar indexing res = index[1] expected = 2 assert res == expected res = index[-1] expected = 18 assert res == expected # slicing # slice value completion index_slice = index[:] expected = index tm.assert_index_equal(index_slice, expected) # positive slice values index_slice = index[7:10:2] expected = Index(np.array([14, 18]), name="foo") tm.assert_index_equal(index_slice, expected) # negative slice values index_slice = index[-1:-5:-2] expected = Index(np.array([18, 14]), name="foo") tm.assert_index_equal(index_slice, expected) # stop overshoot index_slice = index[2:100:4] expected = Index(np.array([4, 12]), name="foo") tm.assert_index_equal(index_slice, expected) # reverse index_slice = index[::-1] expected = Index(index.values[::-1], name="foo") tm.assert_index_equal(index_slice, expected) index_slice = index[-8::-1] expected = Index(np.array([4, 2, 0]), name="foo") tm.assert_index_equal(index_slice, expected) index_slice = index[-40::-1] expected = Index(np.array([], dtype=np.int64), name="foo") tm.assert_index_equal(index_slice, expected) index_slice = index[40::-1] expected = Index(index.values[40::-1], name="foo") tm.assert_index_equal(index_slice, expected) index_slice = index[10::-1] expected = Index(index.values[::-1], name="foo") tm.assert_index_equal(index_slice, expected) @pytest.mark.parametrize("step", set(range(-5, 6)) - {0}) def test_len_specialised(self, step): # make sure that our len is the same as np.arange calc start, stop = (0, 5) if step > 0 else (5, 0) arr = np.arange(start, stop, step) index = RangeIndex(start, stop, step) assert len(index) == len(arr) index = RangeIndex(stop, start, step) assert len(index) == 0 @pytest.fixture( params=[ ([RI(1, 12, 5)], RI(1, 12, 5)), ([RI(0, 6, 4)], RI(0, 6, 4)), ([RI(1, 3), RI(3, 7)], RI(1, 7)), ([RI(1, 5, 2), RI(5, 6)], RI(1, 6, 2)), ([RI(1, 3, 2), RI(4, 7, 3)], RI(1, 7, 3)), ([RI(-4, 3, 2), RI(4, 7, 2)], RI(-4, 7, 2)), ([RI(-4, -8), RI(-8, -12)], RI(0, 0)), ([RI(-4, -8), RI(3, -4)], RI(0, 0)), ([RI(-4, -8), RI(3, 5)], RI(3, 5)), ([RI(-4, -2), RI(3, 5)], I64([-4, -3, 3, 4])), ([RI(-2), RI(3, 5)], RI(3, 5)), ([RI(2), RI(2)], I64([0, 1, 0, 1])), ([RI(2), RI(2, 5), RI(5, 8, 4)], RI(0, 6)), ([RI(2), RI(3, 5), RI(5, 8, 4)], I64([0, 1, 3, 4, 5])), ([RI(-2, 2), RI(2, 5), RI(5, 8, 4)], RI(-2, 6)), ([RI(3), I64([-1, 3, 15])], I64([0, 1, 2, -1, 3, 15])), ([RI(3), F64([-1, 3.1, 15.0])], F64([0, 1, 2, -1, 3.1, 15.0])), ([RI(3), OI(["a", None, 14])], OI([0, 1, 2, "a", None, 14])), ([RI(3, 1), OI(["a", None, 14])], OI(["a", None, 14])), ] ) def appends(self, request): """Inputs and expected outputs for RangeIndex.append test""" return request.param def test_append(self, appends): # GH16212 indices, expected = appends result = indices[0].append(indices[1:]) tm.assert_index_equal(result, expected, exact=True) if len(indices) == 2: # Append single item rather than list result2 = indices[0].append(indices[1]) tm.assert_index_equal(result2, expected, exact=True) def test_engineless_lookup(self): # GH 16685 # Standard lookup on RangeIndex should not require the engine to be # created idx = RangeIndex(2, 10, 3) assert idx.get_loc(5) == 1 tm.assert_numpy_array_equal( idx.get_indexer([2, 8]), ensure_platform_int(np.array([0, 2])) ) with pytest.raises(KeyError, match="3"): idx.get_loc(3) assert "_engine" not in idx._cache # Different types of scalars can be excluded immediately, no need to # use the _engine with pytest.raises(KeyError, match="'a'"): idx.get_loc("a") assert "_engine" not in idx._cache def test_format_empty(self): # GH35712 empty_idx = self._holder(0) assert empty_idx.format() == [] assert empty_idx.format(name=True) == [""]