def test_corr1d(data1, data2):
gs1 = Series(data1)
gs2 = Series(data2)
ps1 = gs1.to_pandas()
ps2 = gs2.to_pandas()
got = gs1.corr(gs2)
expected = ps1.corr(ps2)
np.testing.assert_approx_equal(got, expected, significant=8)
def test_series_nlargest_nelem(nelem):
np.random.seed(0)
elems = np.random.random(nelem)
gds = Series(elems).nlargest(nelem)
pds = pd.Series(elems).nlargest(nelem)
assert (pds == gds.to_pandas()).all().all()
def test_series_sort_values_ignore_index(ignore_index):
gsr = Series([1, 3, 5, 2, 4])
psr = gsr.to_pandas()
expect = psr.sort_values(ignore_index=ignore_index)
got = gsr.sort_values(ignore_index=ignore_index)
assert_eq(expect, got)
def test_series_std(ddof):
np.random.seed(0)
arr = np.random.random(100) - 0.5
sr = Series(arr)
pd = sr.to_pandas()
got = sr.std(ddof=ddof)
expect = pd.std(ddof=ddof)
np.testing.assert_approx_equal(expect, got)
def test_operator_func_between_series_logical(dtype, func, scalar_a, scalar_b,
fill_value):
gdf_series_a = Series([scalar_a]).astype(dtype)
gdf_series_b = Series([scalar_b]).astype(dtype)
pdf_series_a = gdf_series_a.to_pandas()
pdf_series_b = gdf_series_b.to_pandas()
gdf_series_result = getattr(gdf_series_a, func)(gdf_series_b,
fill_value=fill_value)
pdf_series_result = getattr(pdf_series_a, func)(pdf_series_b,
fill_value=fill_value)
if scalar_a in [None, np.nan] and scalar_b in [None, np.nan]:
# cudf binary operations will return `None` when both left- and right-
# side values are `None`. It will return `np.nan` when either side is
# `np.nan`. As a consequence, when we convert our gdf => pdf during
# assert_eq, we get a pdf with dtype='object' (all inputs are none).
# to account for this, we use fillna.
gdf_series_result.fillna(func == "ne", inplace=True)
utils.assert_eq(pdf_series_result, gdf_series_result)
