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TEST-modin-project#1961: refactor some reduction tests
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Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>
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@anmyachev
anmyachev committed Aug 27, 2020
1 parent 1527e50 commit 90a3445
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Showing 2 changed files with 34 additions and 157 deletions.
2 changes: 1 addition & 1 deletion .github/workflows/ci.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@ jobs:
with:
python-version: "3.7.x"
architecture: "x64"
- run: pip install "black==19.10b0"
- run: pip install black
- run: black --check --diff modin/

lint-flake8:
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189 changes: 33 additions & 156 deletions modin/pandas/test/test_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3055,7 +3055,9 @@ def test_all_level(self, axis, level, method):
pandas_df.columns = new_col

eval_general(
modin_df, pandas_df, lambda df: getattr(df, method)(axis=axis, level=level),
modin_df,
pandas_df,
lambda df: getattr(df, method)(axis=axis, level=level),
)

@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
Expand Down Expand Up @@ -3213,180 +3215,55 @@ def test_describe_dtypes(self):
"col3": list("abc"),
"col4": [1, 2, 3],
}
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

modin_result = modin_df.describe()
pandas_result = pandas_df.describe()

df_equals(modin_result, pandas_result)
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
eval_general(modin_df, pandas_df, lambda df: df.describe())

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize("is_transposed", [False, True])
@pytest.mark.parametrize(
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
)
def test_idxmax(self, data, axis, skipna):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

pandas_result = pandas_df.idxmax(axis=axis, skipna=skipna)
modin_result = modin_df.idxmax(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)

pandas_result = pandas_df.T.idxmax(axis=axis, skipna=skipna)
modin_result = modin_df.T.idxmax(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize(
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
)
def test_idxmin(self, data, axis, skipna):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

modin_result = modin_df.idxmin(axis=axis, skipna=skipna)
pandas_result = pandas_df.idxmin(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)

modin_result = modin_df.T.idxmin(axis=axis, skipna=skipna)
pandas_result = pandas_df.T.idxmin(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)
@pytest.mark.parametrize("method", ["idxmin", "idxmax"])
@pytest.mark.parametrize("data", [test_data["dense_nan_data"]])
def test_idxmin_idxmax(self, data, method, axis, skipna, is_transposed):
eval_general(
*create_test_dfs(data),
lambda df: getattr((df.T if is_transposed else df), method)(
axis=axis, skipna=skipna
),
)

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_last_valid_index(self, data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

assert modin_df.last_valid_index() == (pandas_df.last_valid_index())

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize(
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
)
@pytest.mark.parametrize(
"numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)
)
def test_max(self, request, data, axis, skipna, numeric_only):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

try:
pandas_result = pandas_df.max(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception:
with pytest.raises(TypeError):
modin_df.max(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.max(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)

try:
pandas_result = pandas_df.T.max(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception:
with pytest.raises(TypeError):
modin_df.T.max(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.T.max(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize(
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
)
@pytest.mark.parametrize(
"numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)
)
def test_mean(self, request, data, axis, skipna, numeric_only):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

try:
pandas_result = pandas_df.mean(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception as e:
with pytest.raises(type(e)):
modin_df.mean(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.mean(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)

try:
pandas_result = pandas_df.T.mean(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception as e:
with pytest.raises(type(e)):
modin_df.T.mean(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.T.mean(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
assert modin_df.last_valid_index() == pandas_df.last_valid_index()

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize(
"index", bool_arg_values, ids=arg_keys("index", bool_arg_keys)
)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_memory_usage(self, data, index):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data) # noqa F841

modin_result = modin_df.memory_usage(index=index)
pandas_result = pandas_df.memory_usage(index=index)
df_equals(modin_result, pandas_result)
eval_general(*create_test_dfs(data), lambda df: df.memory_usage(index=index))

@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize("is_transposed", [False, True])
@pytest.mark.parametrize(
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
"numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)
)
@pytest.mark.parametrize(
"numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)
"skipna", bool_arg_values, ids=arg_keys("skipna", bool_arg_keys)
)
def test_min(self, data, axis, skipna, numeric_only):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)

try:
pandas_result = pandas_df.min(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception:
with pytest.raises(TypeError):
modin_df.min(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.min(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)

try:
pandas_result = pandas_df.T.min(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
except Exception:
with pytest.raises(TypeError):
modin_df.T.min(axis=axis, skipna=skipna, numeric_only=numeric_only)
else:
modin_result = modin_df.T.min(
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize("method", ["min", "max", "mean"])
@pytest.mark.parametrize("data", [test_data["dense_nan_data"]])
def test_min_max_mean(
self, data, method, axis, skipna, numeric_only, is_transposed
):
eval_general(
*create_test_dfs(data),
lambda df: getattr((df.T if is_transposed else df), method)(
axis=axis, skipna=skipna, numeric_only=numeric_only
)
df_equals(modin_result, pandas_result)
),
)

@pytest.mark.skipif(
os.name == "nt",
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