def test_nans(self):
"""
bottleneck.partsort doesn't handle nans. We need to correct for them.
the arg version is trickiers since we need to make sure to
translate back into the nan-filled array
"""
nanarr = np.arange(10).astype(float)
nanarr[nanarr % 2 == 0] = np.nan
test = topper.topn(nanarr, 3)
correct = [9, 7, 5]
tm.assert_almost_equal(test, correct)
test = topper.topn(nanarr, -3)
correct = [1, 3, 5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, 3)
correct = [9, 7, 5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, -3)
correct = [1, 3, 5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, -3, ascending=False)
correct = [5, 3, 1]
tm.assert_almost_equal(test, correct)
def test_nans(self):
"""
bottleneck.partsort doesn't handle nans. We need to correct for them.
the arg version is trickiers since we need to make sure to
translate back into the nan-filled array
"""
nanarr = np.arange(10).astype(float)
nanarr[nanarr % 2 == 0] = np.nan
test = topper.topn(nanarr, 3)
correct = [9,7,5]
tm.assert_almost_equal(test, correct)
test = topper.topn(nanarr, -3)
correct = [1,3,5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, 3)
correct = [9,7,5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, -3)
correct = [1,3,5]
tm.assert_almost_equal(test, correct)
test = topper.topargn(nanarr, -3, ascending=False)
correct = [5,3,1]
tm.assert_almost_equal(test, correct)
def test_test_ndim(self):
"""
Make sure topn and topargn doesn't accept DataFrame
"""
try:
topper.topn(df, 1)
except:
pass
else:
assert False
try:
topper.topargn(df, 1)
except:
pass
else:
assert False
def test_test_ndim(self):
"""
Make sure topn and topargn doesn't accept DataFrame
"""
try:
topper.topn(df, 1)
except:
pass
else:
assert False
try:
topper.topargn(df, 1)
except:
pass
else:
assert False