def test_quantile(request, data, q):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if not name_contains(request.node.name, no_numeric_dfs):
df_equals(modin_df.quantile(q), pandas_df.quantile(q))
df_equals(modin_df.quantile(q, axis=1), pandas_df.quantile(q, axis=1))
try:
pandas_result = pandas_df.quantile(q, axis=1, numeric_only=False)
except Exception as e:
with pytest.raises(type(e)):
modin_df.quantile(q, axis=1, numeric_only=False)
else:
modin_result = modin_df.quantile(q, axis=1, numeric_only=False)
df_equals(modin_result, pandas_result)
else:
with pytest.raises(ValueError):
modin_df.quantile(q)
if not name_contains(request.node.name, no_numeric_dfs):
df_equals(modin_df.T.quantile(q), pandas_df.T.quantile(q))
df_equals(modin_df.T.quantile(q, axis=1), pandas_df.T.quantile(q, axis=1))
try:
pandas_result = pandas_df.T.quantile(q, axis=1, numeric_only=False)
except Exception as e:
with pytest.raises(type(e)):
modin_df.T.quantile(q, axis=1, numeric_only=False)
else:
modin_result = modin_df.T.quantile(q, axis=1, numeric_only=False)
df_equals(modin_result, pandas_result)
else:
with pytest.raises(ValueError):
modin_df.T.quantile(q)
def test_cumsum(request, data, axis, skipna):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
# pandas exhibits weird behavior for this case
# Remove this case when we can pull the error messages from backend
if name_contains(request.node.name, ["datetime_timedelta_data"
]) and (axis == 0 or axis == "rows"):
with pytest.raises(TypeError):
modin_df.cumsum(axis=axis, skipna=skipna)
else:
try:
pandas_result = pandas_df.cumsum(axis=axis, skipna=skipna)
except Exception as e:
with pytest.raises(type(e)):
modin_df.cumsum(axis=axis, skipna=skipna)
else:
modin_result = modin_df.cumsum(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)
if name_contains(request.node.name, ["datetime_timedelta_data"
]) and (axis == 0 or axis == "rows"):
with pytest.raises(TypeError):
modin_df.T.cumsum(axis=axis, skipna=skipna)
else:
try:
pandas_result = pandas_df.T.cumsum(axis=axis, skipna=skipna)
except Exception as e:
with pytest.raises(type(e)):
modin_df.T.cumsum(axis=axis, skipna=skipna)
else:
modin_result = modin_df.T.cumsum(axis=axis, skipna=skipna)
df_equals(modin_result, pandas_result)
def test_agg_numeric(request, data, axis, func):
if name_contains(request.node.name, numeric_agg_funcs) and name_contains(
request.node.name, numeric_dfs
):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
try:
pandas_result = pandas_df.agg(func, axis)
except Exception as e:
with pytest.raises(type(e)):
modin_df.agg(func, axis)
else:
modin_result = modin_df.agg(func, axis)
df_equals(modin_result, pandas_result)
def test_applymap_numeric(request, data, testfunc):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if name_contains(request.node.name, numeric_dfs):
try:
pandas_result = pandas_df.applymap(testfunc)
except Exception as e:
with pytest.raises(type(e)):
modin_df.applymap(testfunc)
else:
modin_result = modin_df.applymap(testfunc)
df_equals(modin_result, pandas_result)
def test_iloc(request, data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if not name_contains(request.node.name, ["empty_data"]):
# Scaler
np.testing.assert_equal(modin_df.iloc[0, 1], pandas_df.iloc[0, 1])
# Series
df_equals(modin_df.iloc[0], pandas_df.iloc[0])
df_equals(modin_df.iloc[1:, 0], pandas_df.iloc[1:, 0])
df_equals(modin_df.iloc[1:2, 0], pandas_df.iloc[1:2, 0])
# DataFrame
df_equals(modin_df.iloc[[1, 2]], pandas_df.iloc[[1, 2]])
# See issue #80
# df_equals(modin_df.iloc[[1, 2], [1, 0]], pandas_df.iloc[[1, 2], [1, 0]])
df_equals(modin_df.iloc[1:2, 0:2], pandas_df.iloc[1:2, 0:2])
# Issue #43
modin_df.iloc[0:3, :]
# Write Item
modin_df.iloc[[1, 2]] = 42
pandas_df.iloc[[1, 2]] = 42
df_equals(modin_df, pandas_df)
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
modin_df.iloc[0] = modin_df.iloc[1]
pandas_df.iloc[0] = pandas_df.iloc[1]
df_equals(modin_df, pandas_df)
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
modin_df.iloc[:, 0] = modin_df.iloc[:, 1]
pandas_df.iloc[:, 0] = pandas_df.iloc[:, 1]
df_equals(modin_df, pandas_df)
# From issue #1775
df_equals(
modin_df.iloc[lambda df: df.index.get_indexer_for(df.index[:5])],
pandas_df.iloc[lambda df: df.index.get_indexer_for(df.index[:5])],
)
else:
with pytest.raises(IndexError):
modin_df.iloc[0, 1]
def test_apply_numeric(request, data, func, axis):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if name_contains(request.node.name, numeric_dfs):
try:
pandas_result = pandas_df.apply(func, axis)
except Exception as e:
with pytest.raises(type(e)):
modin_df.apply(func, axis)
else:
modin_result = modin_df.apply(func, axis)
df_equals(modin_result, pandas_result)
if "empty_data" not in request.node.name:
key = modin_df.columns[0]
modin_result = modin_df.apply(lambda df: df.drop(key), axis=1)
pandas_result = pandas_df.apply(lambda df: df.drop(key), axis=1)
df_equals(modin_result, pandas_result)
def test_plot(request, data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if name_contains(request.node.name, numeric_dfs):
# We have to test this way because equality in plots means same object.
zipped_plot_lines = zip(modin_df.plot().lines, pandas_df.plot().lines)
for left, right in zipped_plot_lines:
if isinstance(left.get_xdata(),
np.ma.core.MaskedArray) and isinstance(
right.get_xdata(), np.ma.core.MaskedArray):
assert all((left.get_xdata() == right.get_xdata()).data)
else:
assert np.array_equal(left.get_xdata(), right.get_xdata())
if isinstance(left.get_ydata(),
np.ma.core.MaskedArray) and isinstance(
right.get_ydata(), np.ma.core.MaskedArray):
assert all((left.get_ydata() == right.get_ydata()).data)
else:
assert np.array_equal(left.get_xdata(), right.get_xdata())
def test_clip(request, data, axis):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if name_contains(request.node.name, numeric_dfs):
ind_len = (
len(modin_df.index)
if not pandas.DataFrame()._get_axis_number(axis)
else len(modin_df.columns)
)
# set bounds
lower, upper = np.sort(random_state.random_integers(RAND_LOW, RAND_HIGH, 2))
lower_list = random_state.random_integers(RAND_LOW, RAND_HIGH, ind_len)
upper_list = random_state.random_integers(RAND_LOW, RAND_HIGH, ind_len)
# test only upper scalar bound
modin_result = modin_df.clip(None, upper, axis=axis)
pandas_result = pandas_df.clip(None, upper, axis=axis)
df_equals(modin_result, pandas_result)
# test lower and upper scalar bound
modin_result = modin_df.clip(lower, upper, axis=axis)
pandas_result = pandas_df.clip(lower, upper, axis=axis)
df_equals(modin_result, pandas_result)
# test lower and upper list bound on each column
modin_result = modin_df.clip(lower_list, upper_list, axis=axis)
pandas_result = pandas_df.clip(lower_list, upper_list, axis=axis)
df_equals(modin_result, pandas_result)
# test only upper list bound on each column
modin_result = modin_df.clip(np.nan, upper_list, axis=axis)
pandas_result = pandas_df.clip(np.nan, upper_list, axis=axis)
df_equals(modin_result, pandas_result)
with pytest.raises(ValueError):
modin_df.clip(lower=[1, 2, 3], axis=None)
def test_sort_values(request, data, axis, ascending, na_position):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
if "empty_data" not in request.node.name and (
(axis == 0 or axis == "over rows")
or name_contains(request.node.name, numeric_dfs)):
index = modin_df.index if axis == 1 or axis == "columns" else modin_df.columns
key = index[0]
modin_result = modin_df.sort_values(
key,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=False,
)
pandas_result = pandas_df.sort_values(
key,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=False,
)
df_equals(modin_result, pandas_result)
modin_df_cp = modin_df.copy()
pandas_df_cp = pandas_df.copy()
modin_df_cp.sort_values(
key,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=True,
)
pandas_df_cp.sort_values(
key,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=True,
)
df_equals(modin_df_cp, pandas_df_cp)
keys = [key, index[-1]]
modin_result = modin_df.sort_values(
keys,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=False,
)
pandas_result = pandas_df.sort_values(
keys,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=False,
)
df_equals(modin_result, pandas_result)
modin_df_cp = modin_df.copy()
pandas_df_cp = pandas_df.copy()
modin_df_cp.sort_values(
keys,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=True,
)
pandas_df_cp.sort_values(
keys,
axis=axis,
ascending=ascending,
na_position=na_position,
inplace=True,
)
df_equals(modin_df_cp, pandas_df_cp)
