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- import numpy as np
- import pytest
- from pandas import DataFrame, Series, SparseDataFrame, bdate_range
- from pandas.core import nanops
- from pandas.core.sparse.api import SparseDtype
- from pandas.util import testing as tm
- @pytest.fixture
- def dates():
- return bdate_range('1/1/2011', periods=10)
- @pytest.fixture
- def empty():
- return SparseDataFrame()
- @pytest.fixture
- def frame(dates):
- data = {'A': [np.nan, np.nan, np.nan, 0, 1, 2, 3, 4, 5, 6],
- 'B': [0, 1, 2, np.nan, np.nan, np.nan, 3, 4, 5, 6],
- 'C': np.arange(10, dtype=np.float64),
- 'D': [0, 1, 2, 3, 4, 5, np.nan, np.nan, np.nan, np.nan]}
- return SparseDataFrame(data, index=dates)
- @pytest.fixture
- def fill_frame(frame):
- values = frame.values.copy()
- values[np.isnan(values)] = 2
- return SparseDataFrame(values, columns=['A', 'B', 'C', 'D'],
- default_fill_value=2,
- index=frame.index)
- def test_apply(frame):
- applied = frame.apply(np.sqrt)
- assert isinstance(applied, SparseDataFrame)
- tm.assert_almost_equal(applied.values, np.sqrt(frame.values))
- # agg / broadcast
- with tm.assert_produces_warning(FutureWarning):
- broadcasted = frame.apply(np.sum, broadcast=True)
- assert isinstance(broadcasted, SparseDataFrame)
- with tm.assert_produces_warning(FutureWarning):
- exp = frame.to_dense().apply(np.sum, broadcast=True)
- tm.assert_frame_equal(broadcasted.to_dense(), exp)
- applied = frame.apply(np.sum)
- tm.assert_series_equal(applied,
- frame.to_dense().apply(nanops.nansum).to_sparse())
- def test_apply_fill(fill_frame):
- applied = fill_frame.apply(np.sqrt)
- assert applied['A'].fill_value == np.sqrt(2)
- def test_apply_empty(empty):
- assert empty.apply(np.sqrt) is empty
- def test_apply_nonuq():
- orig = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
- index=['a', 'a', 'c'])
- sparse = orig.to_sparse()
- res = sparse.apply(lambda s: s[0], axis=1)
- exp = orig.apply(lambda s: s[0], axis=1)
- # dtype must be kept
- assert res.dtype == SparseDtype(np.int64)
- # ToDo: apply must return subclassed dtype
- assert isinstance(res, Series)
- tm.assert_series_equal(res.to_dense(), exp)
- # df.T breaks
- sparse = orig.T.to_sparse()
- res = sparse.apply(lambda s: s[0], axis=0) # noqa
- exp = orig.T.apply(lambda s: s[0], axis=0)
- # TODO: no non-unique columns supported in sparse yet
- # tm.assert_series_equal(res.to_dense(), exp)
- def test_applymap(frame):
- # just test that it works
- result = frame.applymap(lambda x: x * 2)
- assert isinstance(result, SparseDataFrame)
- def test_apply_keep_sparse_dtype():
- # GH 23744
- sdf = SparseDataFrame(np.array([[0, 1, 0], [0, 0, 0], [0, 0, 1]]),
- columns=['b', 'a', 'c'], default_fill_value=1)
- df = DataFrame(sdf)
- expected = sdf.apply(np.exp)
- result = df.apply(np.exp)
- tm.assert_frame_equal(expected, result)
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