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tmnhat2001
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Fix PEP8 issue
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‎pandas/tests/test_categorical.py

Lines changed: 58 additions & 58 deletions
Original file line numberDiff line numberDiff line change
@@ -798,69 +798,69 @@ def test_set_categories_inplace(self):
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tm.assert_index_equal(cat.categories, pd.Index(['a', 'b', 'c', 'd']))
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@pytest.mark.parametrize(
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"input1, input2, cat_array",
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[
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('int_')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('int_')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('int_'))
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),
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('uint')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('uint')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('uint'))
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"input1, input2, cat_array",
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[
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('int_')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('int_')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('int_'))
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),
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('uint')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('uint')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('uint'))
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),
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('float_')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('float_')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('float_'))
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),
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(
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np.array(
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[1, 2, 3, 3], dtype=np.dtype('unicode_')
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),
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(
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np.array([1, 2, 3, 3], dtype=np.dtype('float_')),
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np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('float_')),
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np.array([1, 2, 3, 4, 5], dtype=np.dtype('float_'))
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np.array(
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[1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('unicode_')
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),
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(
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np.array(
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[1, 2, 3, 3], dtype=np.dtype('unicode_')
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),
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np.array(
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[1, 2, 3, 5, 3, 2, 4], dtype=np.dtype('unicode_')
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),
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np.array(
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[1, 2, 3, 4, 5], dtype=np.dtype('unicode_')
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)
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np.array(
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[1, 2, 3, 4, 5], dtype=np.dtype('unicode_')
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)
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),
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(
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-03-01 10:00:00'
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],
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dtype='datetime64'
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),
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(
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-03-01 10:00:00'
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],
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dtype='datetime64'
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),
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-05-01 10:00:00',
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'2017-03-01 10:00:00', '2017-02-01 10:00:00',
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'2017-04-01 10:00:00'
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],
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dtype='datetime64'
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),
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-04-01 10:00:00',
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'2017-05-01 10:00:00'
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],
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dtype='datetime64'
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)
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-05-01 10:00:00',
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'2017-03-01 10:00:00', '2017-02-01 10:00:00',
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'2017-04-01 10:00:00'
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],
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dtype='datetime64'
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),
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(
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pd.to_timedelta(['1 days', '2 days', '3 days', '3 days'],
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unit="D"),
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pd.to_timedelta(['1 days', '2 days', '3 days', '5 days',
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'3 days', '2 days', '4 days'], unit="D"),
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pd.timedelta_range("1 days", periods=5, freq="D")
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np.array(
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[
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'2017-01-01 10:00:00', '2017-02-01 10:00:00',
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'2017-03-01 10:00:00', '2017-04-01 10:00:00',
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'2017-05-01 10:00:00'
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],
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dtype='datetime64'
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)
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]
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)
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),
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(
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pd.to_timedelta(['1 days', '2 days', '3 days', '3 days'],
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unit="D"),
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pd.to_timedelta(['1 days', '2 days', '3 days', '5 days',
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'3 days', '2 days', '4 days'], unit="D"),
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pd.timedelta_range("1 days", periods=5, freq="D")
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)
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]
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)
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@pytest.mark.parametrize("is_ordered", [True, False])
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def test_drop_duplicates_non_bool(self, input1, input2,
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cat_array, is_ordered):

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