92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
import pytest
|
|
import numpy as np
|
|
from pandas import SparseDataFrame, DataFrame, Series, bdate_range
|
|
from pandas.core import nanops
|
|
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))
|
|
|
|
|
|
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 == 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)
|