def test_index_append_error(data, other):
gd_data = cudf.core.index.as_index(data)
gd_other = cudf.core.index.as_index(other)
got_dtype = (gd_other.dtype
if gd_data.dtype == np.dtype("object") else gd_data.dtype)
with pytest.raises(
TypeError,
match=re.escape(f"cudf does not support appending an Index of "
f"dtype `{np.dtype('object')}` with an Index "
f"of dtype `{got_dtype}`, please type-cast "
f"either one of them to same dtypes."),
):
gd_data.append(gd_other)
with pytest.raises(
TypeError,
match=re.escape(f"cudf does not support appending an Index of "
f"dtype `{np.dtype('object')}` with an Index "
f"of dtype `{got_dtype}`, please type-cast "
f"either one of them to same dtypes."),
):
gd_other.append(gd_data)
sr = gd_other.to_series()
assert_exceptions_equal(
lfunc=gd_data.to_pandas().append,
rfunc=gd_data.append,
lfunc_args_and_kwargs=([[sr.to_pandas()]], ),
rfunc_args_and_kwargs=([[sr]], ),
expected_error_message=r"all inputs must be Index",
)
def test_fillna_categorical(psr_data, fill_value, inplace):
psr = psr_data.copy(deep=True)
gsr = Series.from_pandas(psr)
if isinstance(fill_value, pd.Series):
fill_value_cudf = cudf.from_pandas(fill_value)
else:
fill_value_cudf = fill_value
if (isinstance(fill_value_cudf, cudf.Series)
and gsr.dtype != fill_value_cudf.dtype):
assert_exceptions_equal(
lfunc=psr.fillna,
rfunc=gsr.fillna,
lfunc_args_and_kwargs=([fill_value], {
"inplace": inplace
}),
rfunc_args_and_kwargs=([fill_value_cudf], {
"inplace": inplace
}),
)
else:
expected = psr.fillna(fill_value, inplace=inplace)
got = gsr.fillna(fill_value_cudf, inplace=inplace)
if inplace:
expected = psr
got = gsr
assert_eq(expected, got)
def test_categorical_remove_categories(pd_str_cat, inplace):
pd_sr = pd.Series(pd_str_cat.copy())
cd_sr = cudf.Series(pd_str_cat.copy())
assert_eq(pd_sr, cd_sr)
assert str(pd_sr) == str(cd_sr)
pd_sr_1 = pd_sr.cat.remove_categories(["a"], inplace=inplace)
cd_sr_1 = cd_sr.cat.remove_categories(["a"], inplace=inplace)
pd_sr_1 = pd_sr if pd_sr_1 is None else pd_sr_1
cd_sr_1 = cd_sr if cd_sr_1 is None else cd_sr_1
assert "a" not in pd_sr_1.cat.categories.to_list()
assert "a" not in cd_sr_1.cat.categories.to_pandas().to_list()
assert_eq(pd_sr_1, cd_sr_1)
# test using ordered operators
assert_exceptions_equal(
lfunc=cd_sr.to_pandas().cat.remove_categories,
rfunc=cd_sr.cat.remove_categories,
lfunc_args_and_kwargs=([["a", "d"]], {"inplace": inplace}),
rfunc_args_and_kwargs=([["a", "d"]], {"inplace": inplace}),
expected_error_message="removals must all be in old categories",
)
def test_datetime_series_ops_with_scalars(data, other_scalars, dtype, op):
gsr = cudf.Series(data=data, dtype=dtype)
psr = gsr.to_pandas()
if op == "add":
expected = psr + other_scalars
actual = gsr + other_scalars
elif op == "sub":
expected = psr - other_scalars
actual = gsr - other_scalars
assert_eq(expected, actual)
if op == "add":
expected = other_scalars + psr
actual = other_scalars + gsr
assert_eq(expected, actual)
elif op == "sub":
assert_exceptions_equal(
lfunc=operator.sub,
rfunc=operator.sub,
lfunc_args_and_kwargs=([other_scalars, psr], ),
rfunc_args_and_kwargs=([other_scalars, gsr], ),
compare_error_message=False,
)
def test_categorical_compare_unordered():
cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
pdsr = pd.Series(cat)
sr = cudf.Series(cat)
# test equal
out = sr == sr
assert out.dtype == np.bool_
assert type(out[0]) == np.bool_
assert np.all(out.to_array())
assert np.all(pdsr == pdsr)
# test inequality
out = sr != sr
assert not np.any(out.to_array())
assert not np.any(pdsr != pdsr)
assert not pdsr.cat.ordered
assert not sr.cat.ordered
# test using ordered operators
assert_exceptions_equal(
lfunc=operator.lt,
rfunc=operator.lt,
lfunc_args_and_kwargs=([pdsr, pdsr],),
rfunc_args_and_kwargs=([sr, sr],),
)
def test_series_drop_raises():
gs = cudf.Series([10, 20, 30], index=["x", "y", "z"], name="c")
ps = gs.to_pandas()
assert_exceptions_equal(
lfunc=ps.drop,
rfunc=gs.drop,
lfunc_args_and_kwargs=(["p"],),
rfunc_args_and_kwargs=(["p"],),
expected_error_message="One or more values not found in axis",
)
# dtype specified mismatch
assert_exceptions_equal(
lfunc=ps.drop,
rfunc=gs.drop,
lfunc_args_and_kwargs=([3],),
rfunc_args_and_kwargs=([3],),
expected_error_message="One or more values not found in axis",
)
expect = ps.drop("p", errors="ignore")
actual = gs.drop("p", errors="ignore")
assert_eq(actual, expect)
def test_categorical_reductions(op):
gsr = cudf.Series([1, 2, 3, None], dtype="category")
psr = gsr.to_pandas()
utils.assert_exceptions_equal(
getattr(psr, op), getattr(gsr, op), compare_error_message=False
)
def test_cat_series_binop_error():
df = cudf.DataFrame()
df["a"] = pd.Categorical(list("aababcabbc"), categories=list("abc"))
df["b"] = np.arange(len(df))
dfa = df["a"]
dfb = df["b"]
# lhs is a categorical
assert_exceptions_equal(
lfunc=operator.add,
rfunc=operator.add,
lfunc_args_and_kwargs=([dfa, dfb],),
rfunc_args_and_kwargs=([dfa, dfb],),
check_exception_type=False,
expected_error_message="Series of dtype `category` cannot "
"perform the operation: add",
)
# if lhs is a numerical
assert_exceptions_equal(
lfunc=operator.add,
rfunc=operator.add,
lfunc_args_and_kwargs=([dfb, dfa],),
rfunc_args_and_kwargs=([dfb, dfa],),
check_exception_type=False,
expected_error_message="'add' operator not supported",
)
def test_groupby_nonempty_no_keys(pdf):
gdf = cudf.from_pandas(pdf)
assert_exceptions_equal(
lambda: pdf.groupby([]),
lambda: gdf.groupby([]),
compare_error_message=False,
)
def test_timedelta_ops_datetime_inputs(datetime_data, timedelta_data,
datetime_dtype, timedelta_dtype, ops):
gsr_datetime = cudf.Series(datetime_data, dtype=datetime_dtype)
gsr_timedelta = cudf.Series(timedelta_data, dtype=timedelta_dtype)
psr_datetime = gsr_datetime.to_pandas()
psr_timedelta = gsr_timedelta.to_pandas()
expected = getattr(psr_datetime, ops)(psr_timedelta)
actual = getattr(gsr_datetime, ops)(gsr_timedelta)
assert_eq(expected, actual)
if ops == "add":
expected = getattr(psr_timedelta, ops)(psr_datetime)
actual = getattr(gsr_timedelta, ops)(gsr_datetime)
assert_eq(expected, actual)
elif ops == "sub":
assert_exceptions_equal(
lfunc=operator.sub,
rfunc=operator.sub,
lfunc_args_and_kwargs=([psr_timedelta, psr_datetime], ),
rfunc_args_and_kwargs=([gsr_timedelta, gsr_datetime], ),
expected_error_message=re.escape(
f"Subtraction of {gsr_timedelta.dtype} with "
f"{gsr_datetime.dtype} cannot be performed."),
)
def test_datetime_unsupported_reductions(op):
gsr = cudf.Series([1, 2, 3, None], dtype="datetime64[ns]")
psr = gsr.to_pandas()
utils.assert_exceptions_equal(
lfunc=getattr(psr, op),
rfunc=getattr(gsr, op),
)
def test_to_datetime_errors(data):
pd_data = data
if isinstance(pd_data, (pd.Series, pd.DataFrame, pd.Index)):
gd_data = cudf.from_pandas(pd_data)
else:
gd_data = pd_data
assert_exceptions_equal(pd.to_datetime, cudf.to_datetime, ([pd_data], ),
([gd_data], ))
def test_multiindex_sample_basic(n, frac, replace, axis):
# as we currently don't support column with same name
if axis == 1 and replace:
return
pdf = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5],
"float": [0.05, 0.2, 0.3, 0.2, 0.25],
"int": [1, 3, 5, 4, 2],
},
)
mul_index = cudf.Index(DataFrame.from_pandas(pdf))
random_state = 0
try:
pout = pdf.sample(
n=n,
frac=frac,
replace=replace,
random_state=random_state,
axis=axis,
)
except BaseException:
assert_exceptions_equal(
lfunc=pdf.sample,
rfunc=mul_index.sample,
lfunc_args_and_kwargs=(
[],
{
"n": n,
"frac": frac,
"replace": replace,
"random_state": random_state,
"axis": axis,
},
),
rfunc_args_and_kwargs=(
[],
{
"n": n,
"frac": frac,
"replace": replace,
"random_state": random_state,
"axis": axis,
},
),
)
else:
gout = mul_index.sample(
n=n,
frac=frac,
replace=replace,
random_state=random_state,
axis=axis,
)
assert pout.shape == gout.shape
def test_index_difference_sort_error():
pdi = pd.Index([1, 2, 3])
gdi = cudf.Index([1, 2, 3])
assert_exceptions_equal(
pdi.difference,
gdi.difference,
([pdi], {"sort": True}),
([gdi], {"sort": True}),
)
def test_categorical_setitem_invalid():
ps = pd.Series([1, 2, 3], dtype="category")
gs = cudf.Series([1, 2, 3], dtype="category")
assert_exceptions_equal(
lfunc=ps.__setitem__,
rfunc=gs.__setitem__,
lfunc_args_and_kwargs=([0, 5], {}),
rfunc_args_and_kwargs=([0, 5], {}),
)
def test_multiindex_rename_error(names):
pi = pd.MultiIndex.from_product([["python", "cobra"], [2018, 2019]])
gi = cudf.from_pandas(pi)
assert_exceptions_equal(
lfunc=pi.rename,
rfunc=gi.rename,
lfunc_args_and_kwargs=([], {"names": names}),
rfunc_args_and_kwargs=([], {"names": names}),
)
def test_series_fillna_error():
psr = pd.Series([1, 2, None, 3, None])
gsr = cudf.from_pandas(psr)
assert_exceptions_equal(
psr.fillna,
gsr.fillna,
([pd.DataFrame({"a": [1, 2, 3]})], ),
([cudf.DataFrame({"a": [1, 2, 3]})], ),
)
def test_index_set_names_error(idx, level, names):
pi = idx.copy()
gi = cudf.from_pandas(idx)
assert_exceptions_equal(
lfunc=pi.set_names,
rfunc=gi.set_names,
lfunc_args_and_kwargs=([], {"names": names, "level": level}),
rfunc_args_and_kwargs=([], {"names": names, "level": level}),
)
def test_raise_data_error():
pdf = pd.DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
gdf = cudf.from_pandas(pdf)
assert_exceptions_equal(
pdf.groupby("a").mean,
gdf.groupby("a").mean,
compare_error_message=False,
)
def test_timedelta_unsupported_reductions(op):
gsr = cudf.Series([1, 2, 3, None], dtype="timedelta64[ns]")
psr = gsr.to_pandas()
utils.assert_exceptions_equal(
lfunc=getattr(psr, op),
rfunc=getattr(gsr, op),
expected_error_message=re.escape("cannot perform " +
("kurtosis" if op == "kurt" else op) +
" with type timedelta64[ns]"),
)
def test_column_view_invalid_numeric_to_numeric(data, from_dtype, to_dtype):
cpu_data = np.asarray(data, dtype=from_dtype)
gpu_data = as_column(data, dtype=from_dtype)
assert_exceptions_equal(
lfunc=cpu_data.view,
rfunc=gpu_data.view,
lfunc_args_and_kwargs=([to_dtype], ),
rfunc_args_and_kwargs=([to_dtype], ),
expected_error_message="Can not divide",
)
def test_multiindex_loc_rows_0(pdf, gdf, pdfIndex):
gdfIndex = cudf.from_pandas(pdfIndex)
pdf.index = pdfIndex
gdf.index = gdfIndex
assert_exceptions_equal(
lfunc=pdf.loc.__getitem__,
rfunc=gdf.loc.__getitem__,
lfunc_args_and_kwargs=([(("d", ), slice(None, None, None))], ),
rfunc_args_and_kwargs=([(("d", ), slice(None, None, None))], ),
)
def test_duplicated_with_misspelled_column_name(subset):
df = DataFrame({"A": [0, 0, 1], "B": [0, 0, 1], "C": [0, 0, 1]})
gdf = cudf.DataFrame.from_pandas(df)
assert_exceptions_equal(
lfunc=df.drop_duplicates,
rfunc=gdf.drop_duplicates,
lfunc_args_and_kwargs=([subset], ),
rfunc_args_and_kwargs=([subset], ),
compare_error_message=False,
)
def test_add_categories_error(data, add):
pds = pd.Series(data, dtype="category")
gds = cudf.Series(data, dtype="category")
assert_exceptions_equal(
pds.cat.add_categories,
gds.cat.add_categories,
([add],),
([add],),
compare_error_message=False,
)
def test_datetime_to_datetime_error():
assert_exceptions_equal(
lfunc=pd.to_datetime,
rfunc=cudf.to_datetime,
lfunc_args_and_kwargs=(["02-Oct-2017 09:30", "%d-%B-%Y %H:%M"], ),
rfunc_args_and_kwargs=(["02-Oct-2017 09:30", "%d-%B-%Y %H:%M"], ),
check_exception_type=False,
expected_error_message=re.escape(
"errors parameter has to be either one of: ['ignore', 'raise', "
"'coerce', 'warn'], found: %d-%B-%Y %H:%M"),
)
def test_dataframe_loc_outbound():
df = DataFrame()
size = 10
df["a"] = ha = np.random.randint(low=0, high=100,
size=size).astype(np.int32)
df["b"] = hb = np.random.random(size).astype(np.float32)
pdf = pd.DataFrame()
pdf["a"] = ha
pdf["b"] = hb
assert_exceptions_equal(lambda: pdf.loc[11], lambda: df.loc[11])
def test_df_different_index_shape(df2, binop):
df1 = cudf.DataFrame([1, 2, 3], index=[1, 2, 3])
pdf1 = df1.to_pandas()
pdf2 = df2.to_pandas()
utils.assert_exceptions_equal(
lfunc=binop,
rfunc=binop,
lfunc_args_and_kwargs=([pdf1, pdf2], ),
rfunc_args_and_kwargs=([df1, df2], ),
)
def test_pandas_concat_compatibility_axis1_eq_index():
s1 = gd.Series(["a", "b", "c"], index=[0, 1, 2])
s2 = gd.Series(["a", "b", "c"], index=[1, 1, 1])
ps1 = s1.to_pandas()
ps2 = s2.to_pandas()
assert_exceptions_equal(
lfunc=pd.concat,
rfunc=gd.concat,
lfunc_args_and_kwargs=([], {"objs": [ps1, ps2], "axis": 1}),
rfunc_args_and_kwargs=([], {"objs": [s1, s2], "axis": 1}),
)
def test_multiindex_set_names_error(level, names):
pi = pd.MultiIndex.from_product(
[["python", "cobra"], [2018, 2019], ["aab", "bcd"]]
)
gi = cudf.from_pandas(pi)
assert_exceptions_equal(
lfunc=pi.set_names,
rfunc=gi.set_names,
lfunc_args_and_kwargs=([], {"names": names, "level": level}),
rfunc_args_and_kwargs=([], {"names": names, "level": level}),
)
def test_timedelta_datetime_cast_invalid():
sr = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
psr = sr.to_pandas()
assert_exceptions_equal(psr.astype, sr.astype, (["datetime64[ns]"], ),
(["datetime64[ns]"], ))
sr = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
psr = sr.to_pandas()
assert_exceptions_equal(psr.astype, sr.astype, (["timedelta64[ns]"], ),
(["timedelta64[ns]"], ))