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Comprehensive tests for all groupby rank args
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‎pandas/tests/groupby/test_groupby.py

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@@ -1895,6 +1895,168 @@ def test_rank_apply(self):
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expected = expected.reindex(result.index)
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assert_series_equal(result, expected)
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@pytest.mark.parametrize("vals", [
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[2, 2, 8, 2, 6], ['bar', 'bar', 'foo', 'bar', 'baz']])
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@pytest.mark.parametrize("ties_method,ascending,pct,exp", [
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('average', True, False, DataFrame(
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[2., 2., 5., 2., 4.], columns=['val'])),
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('average', True, True, DataFrame(
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[0.4, 0.4, 1.0, 0.4, 0.8], columns=['val'])),
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('average', False, False, DataFrame(
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[4., 4., 1., 4., 2.], columns=['val'])),
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('average', False, True, DataFrame(
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[.8, .8, .2, .8, .4], columns=['val'])),
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('min', True, False, DataFrame(
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[1., 1., 5., 1., 4.], columns=['val'])),
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('min', True, True, DataFrame(
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[0.2, 0.2, 1.0, 0.2, 0.8], columns=['val'])),
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('min', False, False, DataFrame(
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[3., 3., 1., 3., 2.], columns=['val'])),
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('min', False, True, DataFrame(
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[.6, .6, .2, .6, .4], columns=['val'])),
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('max', True, False, DataFrame(
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[3., 3., 5., 3., 4.], columns=['val'])),
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('max', True, True, DataFrame(
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[0.6, 0.6, 1.0, 0.6, 0.8], columns=['val'])),
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('max', False, False, DataFrame(
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[5., 5., 1., 5., 2.], columns=['val'])),
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('max', False, True, DataFrame(
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[1., 1., .2, 1., .4], columns=['val'])),
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('first', True, False, DataFrame(
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[1., 2., 5., 3., 4.], columns=['val'])),
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('first', True, True, DataFrame(
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[0.2, 0.4, 1.0, 0.6, 0.8], columns=['val'])),
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('first', False, False, DataFrame(
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[3., 4., 1., 5., 2.], columns=['val'])),
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('first', False, True, DataFrame(
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[.6, .8, .2, 1., .4], columns=['val'])),
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('dense', True, False, DataFrame(
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[1., 1., 3., 1., 2.], columns=['val'])),
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('dense', True, True, DataFrame(
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[0.2, 0.2, 0.6, 0.2, 0.4], columns=['val'])),
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('dense', False, False, DataFrame(
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[3., 3., 1., 3., 2.], columns=['val'])),
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('dense', False, True, DataFrame(
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[.6, .6, .2, .6, .4], columns=['val'])),
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])
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def test_rank_args(self, vals, ties_method, ascending, pct, exp):
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if ties_method == 'first' and vals[0] == 'bar':
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pytest.xfail("See GH 19482")
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df = DataFrame({'key': ['foo']*5, 'val': vals})
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result = df.groupby('key').rank(method=ties_method, ascending=ascending,
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pct=pct)
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assert_frame_equal(result, exp)
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@pytest.mark.parametrize("vals", [
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[2, 2, np.nan, 8, 2, 6, np.nan, np.nan], # floats
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['bar', 'bar', np.nan, 'foo', 'bar', 'baz', np.nan, np.nan] # objects
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])
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@pytest.mark.parametrize("ties_method,ascending,na_option,pct,exp", [
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('average', True, 'keep', False, DataFrame(
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[2., 2., np.nan, 5., 2., 4., np.nan, np.nan], columns=['val'])),
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('average', True, 'keep', True, DataFrame(
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[0.4, 0.4, np.nan, 1.0, 0.4, 0.8, np.nan, np.nan],
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columns=['val'])),
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('average', False, 'keep', False, DataFrame(
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[4., 4., np.nan, 1., 4., 2., np.nan, np.nan], columns=['val'])),
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('average', False, 'keep', True, DataFrame(
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[.8, 0.8, np.nan, 0.2, 0.8, 0.4, np.nan, np.nan], columns=['val'])),
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('min', True, 'keep', False, DataFrame(
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[1., 1., np.nan, 5., 1., 4., np.nan, np.nan], columns=['val'])),
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('min', True, 'keep', True, DataFrame(
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[0.2, 0.2, np.nan, 1.0, 0.2, 0.8, np.nan, np.nan],
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columns=['val'])),
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('min', False, 'keep', False, DataFrame(
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[3., 3., np.nan, 1., 3., 2., np.nan, np.nan], columns=['val'])),
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('min', False, 'keep', True, DataFrame(
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[.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan], columns=['val'])),
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('max', True, 'keep', False, DataFrame(
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[3., 3., np.nan, 5., 3., 4., np.nan, np.nan], columns=['val'])),
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('max', True, 'keep', True, DataFrame(
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[0.6, 0.6, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan],
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columns=['val'])),
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('max', False, 'keep', False, DataFrame(
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[5., 5., np.nan, 1., 5., 2., np.nan, np.nan], columns=['val'])),
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('max', False, 'keep', True, DataFrame(
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[1., 1., np.nan, 0.2, 1., 0.4, np.nan, np.nan], columns=['val'])),
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('first', True, 'keep', False, DataFrame(
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[1., 2., np.nan, 5., 3., 4., np.nan, np.nan], columns=['val'])),
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('first', True, 'keep', True, DataFrame(
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[0.2, 0.4, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan],
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columns=['val'])),
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('first', False, 'keep', False, DataFrame(
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[3., 4., np.nan, 1., 5., 2., np.nan, np.nan], columns=['val'])),
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('first', False, 'keep', True, DataFrame(
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[.6, 0.8, np.nan, 0.2, 1., 0.4, np.nan, np.nan], columns=['val'])),
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('dense', True, 'keep', False, DataFrame(
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[1., 1., np.nan, 3., 1., 2., np.nan, np.nan], columns=['val'])),
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('dense', True, 'keep', True, DataFrame(
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[0.2, 0.2, np.nan, 0.6, 0.2, 0.4, np.nan, np.nan],
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columns=['val'])),
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('dense', False, 'keep', False, DataFrame(
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[3., 3., np.nan, 1., 3., 2., np.nan, np.nan], columns=['val'])),
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('dense', False, 'keep', True, DataFrame(
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[.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan], columns=['val'])),
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('average', True, 'no_na', False, DataFrame(
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[2., 2., 7., 5., 2., 4., 7., 7.], columns=['val'])),
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('average', True, 'no_na', True, DataFrame(
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[0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875],
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columns=['val'])),
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('average', False, 'no_na', False, DataFrame(
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[4., 4., 7.0, 1., 4., 2., 7.0, 7.0], columns=['val'])),
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('average', False, 'no_na', True, DataFrame(
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[0.5, 0.5, 0.875, 0.125, 0.5, 0.25, 0.875, 0.875], columns=['val'])),
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('min', True, 'no_na', False, DataFrame(
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[1., 1., 6., 5., 1., 4., 6., 6.], columns=['val'])),
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('min', True, 'no_na', True, DataFrame(
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[0.125, 0.125, 0.75, 0.625, 0.125, 0.5, 0.75, 0.75],
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columns=['val'])),
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('min', False, 'no_na', False, DataFrame(
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[3., 3., 6., 1., 3., 2., 6., 6.], columns=['val'])),
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('min', False, 'no_na', True, DataFrame(
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[0.375, 0.375, 0.75, 0.125, 0.375, 0.25, 0.75, 0.75],
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columns=['val'])),
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('max', True, 'no_na', False, DataFrame(
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[3., 3., 8., 5., 3., 4., 8., 8.], columns=['val'])),
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('max', True, 'no_na', True, DataFrame(
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[0.375, 0.375, 1., 0.625, 0.375, 0.5, 1., 1.], columns=['val'])),
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('max', False, 'no_na', False, DataFrame(
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[5., 5., 8., 1., 5., 2., 8., 8.], columns=['val'])),
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('max', False, 'no_na', True, DataFrame(
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[0.625, 0.625, 1., 0.125, 0.625, 0.25, 1., 1.], columns=['val'])),
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('first', True, 'no_na', False, DataFrame(
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[1., 2., 6., 5., 3., 4., 7., 8.], columns=['val'])),
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('first', True, 'no_na', True, DataFrame(
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[0.125, 0.25, 0.75, 0.625, 0.375, 0.5, 0.875, 1.],
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columns=['val'])),
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('first', False, 'no_na', False, DataFrame(
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[3., 4., 6., 1., 5., 2., 7., 8.], columns=['val'])),
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('first', False, 'no_na', True, DataFrame(
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[0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.],
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columns=['val'])),
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('dense', True, 'no_na', False, DataFrame(
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[1., 1., 4., 3., 1., 2., 4., 4.], columns=['val'])),
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('dense', True, 'no_na', True, DataFrame(
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[0.125, 0.125, 0.5, 0.375, 0.125, 0.25, 0.5, 0.5],
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columns=['val'])),
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('dense', False, 'no_na', False, DataFrame(
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[3., 3., 4., 1., 3., 2., 4., 4.], columns=['val'])),
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('dense', False, 'no_na', True, DataFrame(
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[0.375, 0.375, 0.5, 0.125, 0.375, 0.25, 0.5, 0.5],
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columns=['val'])),
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])
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def test_rank_args_missing(self, vals, ties_method, ascending, na_option,
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pct, exp):
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if ties_method == 'first' and vals[0] == 'bar':
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pytest.xfail("See GH 19482")
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df = DataFrame({'key': ['foo']*8, 'val': vals})
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result = df.groupby('key').rank(method=ties_method, ascending=ascending,
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na_option=na_option, pct=pct)
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assert_frame_equal(result, exp)
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def test_dont_clobber_name_column(self):
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df = DataFrame({'key': ['a', 'a', 'a', 'b', 'b', 'b'],
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'name': ['foo', 'bar', 'baz'] * 2})

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