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TEST-#2026: speed up test_join_sort.py #2027

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Sep 7, 2020
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TEST-#2026: speed up test_join_sort.py #2027

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TEST-#2026: speed up test_join_sort.py
anmyachev Sep 7, 2020
b4b9750
TEST-#2026: Update modin/pandas/test/dataframe/test_join_sort.py
anmyachev Sep 7, 2020
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116 changes: 52 additions & 64 deletions modin/pandas/test/dataframe/test_join_sort.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,9 @@
axis_values,
bool_arg_keys,
bool_arg_values,
test_data,
generate_multiindex,
eval_general,
)

pd.DEFAULT_NPARTITIONS = 4
Expand Down Expand Up @@ -306,82 +309,67 @@ def test_merge(test_data, test_data2):
modin_df.merge("Non-valid type")


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize(
"ascending", bool_arg_values, ids=arg_keys("ascending", bool_arg_keys)
)
@pytest.mark.parametrize("na_position", ["first", "last"], ids=["first", "last"])
@pytest.mark.parametrize(
"sort_remaining", bool_arg_values, ids=arg_keys("sort_remaining", bool_arg_keys)
)
def test_sort_index(data, axis, ascending, na_position, sort_remaining):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
def test_sort_index(axis, ascending, na_position):
data = test_data["float_nan_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)

# Change index value so sorting will actually make a difference
if axis == "rows" or axis == 0:
if axis == 0:
length = len(modin_df.index)
modin_df.index = [(i - length / 2) % length for i in range(length)]
pandas_df.index = [(i - length / 2) % length for i in range(length)]
for df in [modin_df, pandas_df]:
df.index = [(i - length / 2) % length for i in range(length)]

# Add NaNs to sorted index
if axis == "rows" or axis == 0:
length = len(modin_df.index)
modin_df.index = [
np.nan if i % 2 == 0 else modin_df.index[i] for i in range(length)
]
pandas_df.index = [
np.nan if i % 2 == 0 else pandas_df.index[i] for i in range(length)
]
else:
length = len(modin_df.columns)
modin_df.columns = [
np.nan if i % 2 == 0 else modin_df.columns[i] for i in range(length)
]
pandas_df.columns = [
np.nan if i % 2 == 0 else pandas_df.columns[i] for i in range(length)
]

modin_result = modin_df.sort_index(
axis=axis, ascending=ascending, na_position=na_position, inplace=False
)
pandas_result = pandas_df.sort_index(
axis=axis, ascending=ascending, na_position=na_position, inplace=False
)
df_equals(modin_result, pandas_result)
for df in [modin_df, pandas_df]:
sort_index = df.axes[axis]
df.set_axis(
[np.nan if i % 2 == 0 else sort_index[i] for i in range(len(sort_index))],
axis=axis,
inplace=True,
)

modin_df_cp = modin_df.copy()
pandas_df_cp = pandas_df.copy()
modin_df_cp.sort_index(
axis=axis, ascending=ascending, na_position=na_position, inplace=True
)
pandas_df_cp.sort_index(
axis=axis, ascending=ascending, na_position=na_position, inplace=True
eval_general(
modin_df,
pandas_df,
lambda df: df.sort_index(
axis=axis, ascending=ascending, na_position=na_position
),
)
df_equals(modin_df_cp, pandas_df_cp)

# MultiIndex
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
modin_df.index = pd.MultiIndex.from_tuples(
[(i // 10, i // 5, i) for i in range(len(modin_df))]
)
pandas_df.index = pandas.MultiIndex.from_tuples(
[(i // 10, i // 5, i) for i in range(len(pandas_df))]
)
modin_df.columns = pd.MultiIndex.from_tuples(
[(i // 10, i // 5, i) for i in range(len(modin_df.columns))]
)
pandas_df.columns = pd.MultiIndex.from_tuples(
[(i // 10, i // 5, i) for i in range(len(pandas_df.columns))]
)

with pytest.warns(UserWarning):
df_equals(modin_df.sort_index(level=0), pandas_df.sort_index(level=0))
with pytest.warns(UserWarning):
df_equals(modin_df.sort_index(axis=0), pandas_df.sort_index(axis=0))
with pytest.warns(UserWarning):
df_equals(modin_df.sort_index(axis=1), pandas_df.sort_index(axis=1))
@pytest.mark.parametrize("axis", ["rows", "columns"])
def test_sort_index_inplace(axis):
data = test_data["int_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)

for df in [modin_df, pandas_df]:
df.sort_index(axis=axis, inplace=True)
df_equals(modin_df, pandas_df)


@pytest.mark.parametrize(
"sort_remaining", bool_arg_values, ids=arg_keys("sort_remaining", bool_arg_keys)
)
def test_sort_multiindex(sort_remaining):
data = test_data["int_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)

for index in ["index", "columns"]:
new_index = generate_multiindex(len(getattr(modin_df, index)))
for df in [modin_df, pandas_df]:
setattr(df, index, new_index)

for kwargs in [{"level": 0}, {"axis": 0}, {"axis": 1}]:
with pytest.warns(UserWarning):
df_equals(
modin_df.sort_index(sort_remaining=sort_remaining, **kwargs),
pandas_df.sort_index(sort_remaining=sort_remaining, **kwargs),
)


@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
Expand Down