|
| 1 | +import numpy as np |
| 2 | +import pytest |
| 3 | + |
| 4 | +from pandas import DataFrame, MultiIndex, Series |
| 5 | +import pandas._testing as tm |
| 6 | + |
| 7 | + |
| 8 | +class TestReorderLevels: |
| 9 | + @pytest.mark.parametrize("klass", [Series, DataFrame]) |
| 10 | + def test_reorder_levels(self, klass): |
| 11 | + index = MultiIndex( |
| 12 | + levels=[["bar"], ["one", "two", "three"], [0, 1]], |
| 13 | + codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], |
| 14 | + names=["L0", "L1", "L2"], |
| 15 | + ) |
| 16 | + df = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=index) |
| 17 | + obj = df if klass is DataFrame else df["A"] |
| 18 | + |
| 19 | + # no change, position |
| 20 | + result = obj.reorder_levels([0, 1, 2]) |
| 21 | + tm.assert_equal(obj, result) |
| 22 | + |
| 23 | + # no change, labels |
| 24 | + result = obj.reorder_levels(["L0", "L1", "L2"]) |
| 25 | + tm.assert_equal(obj, result) |
| 26 | + |
| 27 | + # rotate, position |
| 28 | + result = obj.reorder_levels([1, 2, 0]) |
| 29 | + e_idx = MultiIndex( |
| 30 | + levels=[["one", "two", "three"], [0, 1], ["bar"]], |
| 31 | + codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0]], |
| 32 | + names=["L1", "L2", "L0"], |
| 33 | + ) |
| 34 | + expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx) |
| 35 | + expected = expected if klass is DataFrame else expected["A"] |
| 36 | + tm.assert_equal(result, expected) |
| 37 | + |
| 38 | + result = obj.reorder_levels([0, 0, 0]) |
| 39 | + e_idx = MultiIndex( |
| 40 | + levels=[["bar"], ["bar"], ["bar"]], |
| 41 | + codes=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], |
| 42 | + names=["L0", "L0", "L0"], |
| 43 | + ) |
| 44 | + expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx) |
| 45 | + expected = expected if klass is DataFrame else expected["A"] |
| 46 | + tm.assert_equal(result, expected) |
| 47 | + |
| 48 | + result = obj.reorder_levels(["L0", "L0", "L0"]) |
| 49 | + tm.assert_equal(result, expected) |
| 50 | + |
| 51 | + def test_reorder_levels_swaplevel_equivalence( |
| 52 | + self, multiindex_year_month_day_dataframe_random_data |
| 53 | + ): |
| 54 | + |
| 55 | + ymd = multiindex_year_month_day_dataframe_random_data |
| 56 | + |
| 57 | + result = ymd.reorder_levels(["month", "day", "year"]) |
| 58 | + expected = ymd.swaplevel(0, 1).swaplevel(1, 2) |
| 59 | + tm.assert_frame_equal(result, expected) |
| 60 | + |
| 61 | + result = ymd["A"].reorder_levels(["month", "day", "year"]) |
| 62 | + expected = ymd["A"].swaplevel(0, 1).swaplevel(1, 2) |
| 63 | + tm.assert_series_equal(result, expected) |
| 64 | + |
| 65 | + result = ymd.T.reorder_levels(["month", "day", "year"], axis=1) |
| 66 | + expected = ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1) |
| 67 | + tm.assert_frame_equal(result, expected) |
| 68 | + |
| 69 | + with pytest.raises(TypeError, match="hierarchical axis"): |
| 70 | + ymd.reorder_levels([1, 2], axis=1) |
| 71 | + |
| 72 | + with pytest.raises(IndexError, match="Too many levels"): |
| 73 | + ymd.index.reorder_levels([1, 2, 3]) |
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