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FEAT-modin-project#1222: Better tests for DataFrame.asof().
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Signed-off-by: Itamar Turner-Trauring <itamar@itamarst.org>
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@itamarst
itamarst committed Aug 31, 2020
1 parent 363da6d commit ab2ca74
Showing 1 changed file with 45 additions and 57 deletions.
102 changes: 45 additions & 57 deletions modin/pandas/test/test_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2061,20 +2061,36 @@ def test_asfreq(self):
df.asfreq(freq="30S")

def test_asof(self):
df = pd.DataFrame(
{"a": [10, 20, 30, 40, 50], "b": [None, None, None, None, 500]},
index=pd.DatetimeIndex(
[
"2018-02-27 09:01:00",
"2018-02-27 09:02:00",
"2018-02-27 09:03:00",
"2018-02-27 09:04:00",
"2018-02-27 09:05:00",
]
),
data = {"a": [10, 20, 30, 40, 50], "b": [None, None, None, None, 500]}
index = pd.DatetimeIndex(
[
"2018-02-27 09:01:00",
"2018-02-27 09:02:00",
"2018-02-27 09:03:00",
"2018-02-27 09:04:00",
"2018-02-27 09:05:00",
]
)
modin_df = pd.DataFrame(data, index=index)
pandas_df = pandas.DataFrame(data, index=index)
dates = ["2018-02-27 09:03:30", "2018-02-27 09:04:30"]
modin_dates = pd.DatetimeIndex(dates)
pandas_dates = pandas.DatetimeIndex(dates)
df_equals(modin_df.asof(modin_dates), pandas_df.asof(pandas_dates))
df_equals(
modin_df.asof(modin_dates, subset=["a"]),
pandas_df.asof(pandas_dates, subset=["a"]),
)
df_equals(
modin_df.asof(modin_dates, subset=["b"]),
pandas_df.asof(pandas_dates, subset=["b"]),
)

date = pd.to_datetime(dates[0])
df_equals(modin_df.asof(date), pandas_df.asof(date))
df_equals(
modin_df.asof(date, subset=["a"]), pandas_df.asof(date, subset=["a"]),
)
with pytest.warns(UserWarning):
df.asof(pd.DatetimeIndex(["2018-02-27 09:03:30", "2018-02-27 09:04:30"]))

def test_assign(self):
data = test_data_values[0]
Expand Down Expand Up @@ -2187,8 +2203,7 @@ def test_cov(self):
df_equals(modin_result, pandas_result)

@pytest.mark.skipif(
os.name == "nt",
reason="AssertionError: numpy array are different",
os.name == "nt", reason="AssertionError: numpy array are different",
)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_dot(self, data):
Expand Down Expand Up @@ -2237,8 +2252,7 @@ def test_dot(self, data):
df_equals(modin_result, pandas_result)

@pytest.mark.skipif(
os.name == "nt",
reason="AssertionError: numpy array are different",
os.name == "nt", reason="AssertionError: numpy array are different",
)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_matmul(self, data):
Expand Down Expand Up @@ -2416,9 +2430,7 @@ def test_kurt_kurtosis_level(self, level):
df_modin.columns = index
df_pandas.columns = index
eval_general(
df_modin,
df_pandas,
lambda df: df.kurtosis(axis=1, level=level),
df_modin, df_pandas, lambda df: df.kurtosis(axis=1, level=level),
)

def test_last(self):
Expand Down Expand Up @@ -2457,9 +2469,7 @@ def test_mad_level(self, level):
modin_df.columns = index
pandas_df.columns = index
eval_general(
modin_df,
pandas_df,
lambda df: df.mad(axis=1, level=level),
modin_df, pandas_df, lambda df: df.mad(axis=1, level=level),
)

def test_mask(self):
Expand Down Expand Up @@ -2655,12 +2665,10 @@ def test_resample(self, rule, axis, closed, label, on, level):
pandas_resampler.transform(lambda x: (x - x.mean()) / x.std()),
)
df_equals(
pandas_resampler.aggregate("max"),
modin_resampler.aggregate("max"),
pandas_resampler.aggregate("max"), modin_resampler.aggregate("max"),
)
df_equals(
modin_resampler.apply("sum"),
pandas_resampler.apply("sum"),
modin_resampler.apply("sum"), pandas_resampler.apply("sum"),
)
df_equals(
modin_resampler.get_group(name=list(modin_resampler.groups)[0]),
Expand All @@ -2673,8 +2681,7 @@ def test_resample(self, rule, axis, closed, label, on, level):
# Upsampling from level= or on= selection is not supported
if on is None and level is None:
df_equals(
modin_resampler.interpolate(),
pandas_resampler.interpolate(),
modin_resampler.interpolate(), pandas_resampler.interpolate(),
)
df_equals(modin_resampler.asfreq(), pandas_resampler.asfreq())
df_equals(
Expand Down Expand Up @@ -3179,17 +3186,14 @@ def test_all_any_level(self, data, 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)
@pytest.mark.parametrize("data", [test_data["dense_nan_data"]])
def test_count(self, data, axis):
eval_general(
*create_test_dfs(data),
lambda df: df.count(axis=axis),
*create_test_dfs(data), lambda df: df.count(axis=axis),
)

@pytest.mark.parametrize(
Expand Down Expand Up @@ -3222,17 +3226,14 @@ def test_count_level(self, data, axis, level):
pandas_df.columns = new_col

eval_general(
modin_df,
pandas_df,
lambda df: df.count(axis=axis, level=level),
modin_df, pandas_df, lambda df: df.count(axis=axis, level=level),
)

@pytest.mark.parametrize("percentiles", [None, 0.10, 0.11, 0.44, 0.78, 0.99])
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_describe(self, data, percentiles):
eval_general(
*create_test_dfs(data),
lambda df: df.describe(percentiles=percentiles),
*create_test_dfs(data), lambda df: df.describe(percentiles=percentiles),
)

@pytest.mark.parametrize(
Expand Down Expand Up @@ -3350,18 +3351,12 @@ def test_min_max_mean(
@pytest.mark.parametrize("axis", axis_values, ids=axis_keys)
@pytest.mark.parametrize("data", [test_data["dense_nan_data"]])
def test_prod(
self,
data,
axis,
skipna,
is_transposed,
method,
self, data, axis, skipna, is_transposed, method,
):
eval_general(
*create_test_dfs(data),
lambda df, *args, **kwargs: getattr(df.T if is_transposed else df, method)(
axis=axis,
skipna=skipna,
axis=axis, skipna=skipna,
),
)

Expand Down Expand Up @@ -3393,10 +3388,7 @@ def test_prod_specific(self, min_count, numeric_only):
def test_sum(self, data, axis, skipna, is_transposed):
eval_general(
*create_test_dfs(data),
lambda df: (df.T if is_transposed else df).sum(
axis=axis,
skipna=skipna,
),
lambda df: (df.T if is_transposed else df).sum(axis=axis, skipna=skipna,),
)

@pytest.mark.parametrize(
Expand Down Expand Up @@ -5283,9 +5275,7 @@ def test___setitem__mask(self):
ids=["empty", "empty_columns"],
)
@pytest.mark.parametrize(
"value",
[np.array(["one", "two"]), [11, 22]],
ids=["ndarray", "list"],
"value", [np.array(["one", "two"]), [11, 22]], ids=["ndarray", "list"],
)
@pytest.mark.parametrize("convert_to_series", [False, True])
@pytest.mark.parametrize("new_col_id", [123, "new_col"], ids=["integer", "string"])
Expand Down Expand Up @@ -5577,9 +5567,7 @@ def test_inplace_series_ops(self, data):
modin_df[col0].fillna(0, inplace=True)
df_equals(modin_df, pandas_df)

def test___setattr__(
self,
):
def test___setattr__(self,):
pandas_df = pandas.DataFrame([1, 2, 3])
modin_df = pd.DataFrame([1, 2, 3])

Expand Down

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