106 lines
4 KiB
Python
106 lines
4 KiB
Python
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import pytest
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import numpy as np
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import pandas as pd
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import pandas.util.testing as tm
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from .base import BaseExtensionTests
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class BaseMethodsTests(BaseExtensionTests):
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"""Various Series and DataFrame methods."""
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@pytest.mark.parametrize('dropna', [True, False])
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def test_value_counts(self, all_data, dropna):
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all_data = all_data[:10]
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if dropna:
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other = np.array(all_data[~all_data.isna()])
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else:
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other = all_data
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result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
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expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
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self.assert_series_equal(result, expected)
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def test_count(self, data_missing):
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df = pd.DataFrame({"A": data_missing})
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result = df.count(axis='columns')
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expected = pd.Series([0, 1])
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self.assert_series_equal(result, expected)
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def test_apply_simple_series(self, data):
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result = pd.Series(data).apply(id)
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assert isinstance(result, pd.Series)
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def test_argsort(self, data_for_sorting):
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result = pd.Series(data_for_sorting).argsort()
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expected = pd.Series(np.array([2, 0, 1], dtype=np.int64))
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self.assert_series_equal(result, expected)
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def test_argsort_missing(self, data_missing_for_sorting):
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result = pd.Series(data_missing_for_sorting).argsort()
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expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
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self.assert_series_equal(result, expected)
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@pytest.mark.parametrize('ascending', [True, False])
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def test_sort_values(self, data_for_sorting, ascending):
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ser = pd.Series(data_for_sorting)
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result = ser.sort_values(ascending=ascending)
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expected = ser.iloc[[2, 0, 1]]
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if not ascending:
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expected = expected[::-1]
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self.assert_series_equal(result, expected)
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@pytest.mark.parametrize('ascending', [True, False])
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def test_sort_values_missing(self, data_missing_for_sorting, ascending):
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ser = pd.Series(data_missing_for_sorting)
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result = ser.sort_values(ascending=ascending)
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if ascending:
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expected = ser.iloc[[2, 0, 1]]
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else:
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expected = ser.iloc[[0, 2, 1]]
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self.assert_series_equal(result, expected)
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@pytest.mark.parametrize('ascending', [True, False])
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def test_sort_values_frame(self, data_for_sorting, ascending):
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df = pd.DataFrame({"A": [1, 2, 1],
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"B": data_for_sorting})
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result = df.sort_values(['A', 'B'])
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expected = pd.DataFrame({"A": [1, 1, 2],
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'B': data_for_sorting.take([2, 0, 1])},
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index=[2, 0, 1])
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self.assert_frame_equal(result, expected)
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@pytest.mark.parametrize('box', [pd.Series, lambda x: x])
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@pytest.mark.parametrize('method', [lambda x: x.unique(), pd.unique])
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def test_unique(self, data, box, method):
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duplicated = box(data._from_sequence([data[0], data[0]]))
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result = method(duplicated)
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assert len(result) == 1
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assert isinstance(result, type(data))
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assert result[0] == duplicated[0]
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@pytest.mark.parametrize('na_sentinel', [-1, -2])
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def test_factorize(self, data_for_grouping, na_sentinel):
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labels, uniques = pd.factorize(data_for_grouping,
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na_sentinel=na_sentinel)
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expected_labels = np.array([0, 0, na_sentinel,
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na_sentinel, 1, 1, 0, 2],
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dtype=np.intp)
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expected_uniques = data_for_grouping.take([0, 4, 7])
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tm.assert_numpy_array_equal(labels, expected_labels)
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self.assert_extension_array_equal(uniques, expected_uniques)
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@pytest.mark.parametrize('na_sentinel', [-1, -2])
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def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
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l1, u1 = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
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l2, u2 = data_for_grouping.factorize(na_sentinel=na_sentinel)
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tm.assert_numpy_array_equal(l1, l2)
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self.assert_extension_array_equal(u1, u2)
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