93 lines
2.6 KiB
Python
93 lines
2.6 KiB
Python
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import pytest
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import numpy as np
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from pandas import SparseDataFrame, DataFrame, Series, bdate_range
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from pandas.core import nanops
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from pandas.util import testing as tm
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@pytest.fixture
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def dates():
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return bdate_range('1/1/2011', periods=10)
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@pytest.fixture
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def empty():
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return SparseDataFrame()
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@pytest.fixture
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def frame(dates):
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data = {'A': [np.nan, np.nan, np.nan, 0, 1, 2, 3, 4, 5, 6],
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'B': [0, 1, 2, np.nan, np.nan, np.nan, 3, 4, 5, 6],
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'C': np.arange(10, dtype=np.float64),
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'D': [0, 1, 2, 3, 4, 5, np.nan, np.nan, np.nan, np.nan]}
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return SparseDataFrame(data, index=dates)
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@pytest.fixture
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def fill_frame(frame):
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values = frame.values.copy()
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values[np.isnan(values)] = 2
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return SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
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default_fill_value=2,
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index=frame.index)
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def test_apply(frame):
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applied = frame.apply(np.sqrt)
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assert isinstance(applied, SparseDataFrame)
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tm.assert_almost_equal(applied.values, np.sqrt(frame.values))
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# agg / broadcast
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with tm.assert_produces_warning(FutureWarning):
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broadcasted = frame.apply(np.sum, broadcast=True)
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assert isinstance(broadcasted, SparseDataFrame)
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with tm.assert_produces_warning(FutureWarning):
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exp = frame.to_dense().apply(np.sum, broadcast=True)
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tm.assert_frame_equal(broadcasted.to_dense(), exp)
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applied = frame.apply(np.sum)
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tm.assert_series_equal(applied,
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frame.to_dense().apply(nanops.nansum))
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def test_apply_fill(fill_frame):
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applied = fill_frame.apply(np.sqrt)
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assert applied['A'].fill_value == np.sqrt(2)
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def test_apply_empty(empty):
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assert empty.apply(np.sqrt) is empty
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def test_apply_nonuq():
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orig = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
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index=['a', 'a', 'c'])
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sparse = orig.to_sparse()
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res = sparse.apply(lambda s: s[0], axis=1)
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exp = orig.apply(lambda s: s[0], axis=1)
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# dtype must be kept
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assert res.dtype == np.int64
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# ToDo: apply must return subclassed dtype
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assert isinstance(res, Series)
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tm.assert_series_equal(res.to_dense(), exp)
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# df.T breaks
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sparse = orig.T.to_sparse()
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res = sparse.apply(lambda s: s[0], axis=0) # noqa
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exp = orig.T.apply(lambda s: s[0], axis=0)
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# TODO: no non-unique columns supported in sparse yet
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# tm.assert_series_equal(res.to_dense(), exp)
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def test_applymap(frame):
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# just test that it works
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result = frame.applymap(lambda x: x * 2)
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assert isinstance(result, SparseDataFrame)
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