def test_compare(align_axis, keep_shape, keep_equal):
kwargs = {
"align_axis": align_axis,
"keep_shape": keep_shape,
"keep_equal": keep_equal,
}
frame_data1 = random_state.randn(100, 10)
frame_data2 = random_state.randn(100, 10)
pandas_df = pandas.DataFrame(frame_data1, columns=list("abcdefghij"))
pandas_df2 = pandas.DataFrame(frame_data2, columns=list("abcdefghij"))
modin_df = pd.DataFrame(frame_data1, columns=list("abcdefghij"))
modin_df2 = pd.DataFrame(frame_data2, columns=list("abcdefghij"))
modin_result = modin_df.compare(modin_df2, **kwargs)
pandas_result = pandas_df.compare(pandas_df2, **kwargs)
assert to_pandas(modin_result).equals(pandas_result)
modin_result = modin_df2.compare(modin_df, **kwargs)
pandas_result = pandas_df2.compare(pandas_df, **kwargs)
assert to_pandas(modin_result).equals(pandas_result)
series_data1 = ["a", "b", "c", "d", "e"]
series_data2 = ["a", "a", "c", "b", "e"]
pandas_series1 = pandas.Series(series_data1)
pandas_series2 = pandas.Series(series_data2)
modin_series1 = pd.Series(series_data1)
modin_series2 = pd.Series(series_data2)
modin_result = modin_series1.compare(modin_series2, **kwargs)
pandas_result = pandas_series1.compare(pandas_series2, **kwargs)
assert to_pandas(modin_result).equals(pandas_result)
modin_result = modin_series2.compare(modin_series1, **kwargs)
pandas_result = pandas_series2.compare(pandas_series1, **kwargs)
assert to_pandas(modin_result).equals(pandas_result)
def test_eval_df_arithmetic_subexpression():
frame_data = {"a": random_state.randn(10), "b": random_state.randn(10)}
df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
df.eval("not_e = sin(a + b)", engine="python", parser="pandas", inplace=True)
modin_df.eval("not_e = sin(a + b)", engine="python", parser="pandas", inplace=True)
# TODO: Use a series equality validator.
df_equals(modin_df, df)
def test_fillna_inplace():
frame_data = random_state.randn(10, 4)
df = pandas.DataFrame(frame_data)
df[1][:4] = np.nan
df[3][-4:] = np.nan
modin_df = pd.DataFrame(df)
df.fillna(value=0, inplace=True)
try:
df_equals(modin_df, df)
except AssertionError:
pass
else:
assert False
modin_df.fillna(value=0, inplace=True)
df_equals(modin_df, df)
modin_df = pd.DataFrame(df).fillna(value={0: 0}, inplace=True)
assert modin_df is None
df[1][:4] = np.nan
df[3][-4:] = np.nan
modin_df = pd.DataFrame(df)
df.fillna(method="ffill", inplace=True)
try:
df_equals(modin_df, df)
except AssertionError:
pass
else:
assert False
modin_df.fillna(method="ffill", inplace=True)
df_equals(modin_df, df)
def test_eval_df_use_case():
frame_data = {"a": random_state.randn(10), "b": random_state.randn(10)}
df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
# test eval for series results
tmp_pandas = df.eval("arctan2(sin(a), b)",
engine="python",
parser="pandas")
tmp_modin = modin_df.eval("arctan2(sin(a), b)",
engine="python",
parser="pandas")
assert isinstance(tmp_modin, pd.Series)
df_equals(tmp_modin, tmp_pandas)
# Test not inplace assignments
tmp_pandas = df.eval("e = arctan2(sin(a), b)",
engine="python",
parser="pandas")
tmp_modin = modin_df.eval("e = arctan2(sin(a), b)",
engine="python",
parser="pandas")
df_equals(tmp_modin, tmp_pandas)
# Test inplace assignments
df.eval("e = arctan2(sin(a), b)",
engine="python",
parser="pandas",
inplace=True)
modin_df.eval("e = arctan2(sin(a), b)",
engine="python",
parser="pandas",
inplace=True)
# TODO: Use a series equality validator.
df_equals(modin_df, df)
def test_where():
frame_data = random_state.randn(100, 10)
pandas_df = pandas.DataFrame(frame_data, columns=list("abcdefghij"))
modin_df = pd.DataFrame(frame_data, columns=list("abcdefghij"))
pandas_cond_df = pandas_df % 5 < 2
modin_cond_df = modin_df % 5 < 2
pandas_result = pandas_df.where(pandas_cond_df, -pandas_df)
modin_result = modin_df.where(modin_cond_df, -modin_df)
assert all((to_pandas(modin_result) == pandas_result).all())
other = pandas_df.loc[3]
pandas_result = pandas_df.where(pandas_cond_df, other, axis=1)
modin_result = modin_df.where(modin_cond_df, other, axis=1)
assert all((to_pandas(modin_result) == pandas_result).all())
other = pandas_df["e"]
pandas_result = pandas_df.where(pandas_cond_df, other, axis=0)
modin_result = modin_df.where(modin_cond_df, other, axis=0)
assert all((to_pandas(modin_result) == pandas_result).all())
pandas_result = pandas_df.where(pandas_df < 2, True)
modin_result = modin_df.where(modin_df < 2, True)
assert all((to_pandas(modin_result) == pandas_result).all())
def test_drop():
frame_data = {"A": [1, 2, 3, 4], "B": [0, 1, 2, 3]}
simple = pandas.DataFrame(frame_data)
modin_simple = pd.DataFrame(frame_data)
df_equals(modin_simple.drop("A", axis=1), simple[["B"]])
df_equals(modin_simple.drop(["A", "B"], axis="columns"), simple[[]])
df_equals(modin_simple.drop([0, 1, 3], axis=0), simple.loc[[2], :])
df_equals(modin_simple.drop([0, 3], axis="index"), simple.loc[[1, 2], :])
pytest.raises(ValueError, modin_simple.drop, 5)
pytest.raises(ValueError, modin_simple.drop, "C", 1)
pytest.raises(ValueError, modin_simple.drop, [1, 5])
pytest.raises(ValueError, modin_simple.drop, ["A", "C"], 1)
# errors = 'ignore'
df_equals(modin_simple.drop(5, errors="ignore"), simple)
df_equals(modin_simple.drop([0, 5], errors="ignore"),
simple.loc[[1, 2, 3], :])
df_equals(modin_simple.drop("C", axis=1, errors="ignore"), simple)
df_equals(modin_simple.drop(["A", "C"], axis=1, errors="ignore"),
simple[["B"]])
# non-unique
nu_df = pandas.DataFrame(zip(range(3), range(-3, 1), list("abc")),
columns=["a", "a", "b"])
modin_nu_df = pd.DataFrame(nu_df)
df_equals(modin_nu_df.drop("a", axis=1), nu_df[["b"]])
df_equals(modin_nu_df.drop("b", axis="columns"), nu_df["a"])
df_equals(modin_nu_df.drop([]), nu_df)
nu_df = nu_df.set_index(pandas.Index(["X", "Y", "X"]))
nu_df.columns = list("abc")
modin_nu_df = pd.DataFrame(nu_df)
df_equals(modin_nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :])
df_equals(modin_nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :])
# inplace cache issue
frame_data = random_state.randn(10, 3)
df = pandas.DataFrame(frame_data, columns=list("abc"))
modin_df = pd.DataFrame(frame_data, columns=list("abc"))
expected = df[~(df.b > 0)]
modin_df.drop(labels=df[df.b > 0].index, inplace=True)
df_equals(modin_df, expected)
midx = pd.MultiIndex(
levels=[["lama", "cow", "falcon"], ["speed", "weight", "length"]],
codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
)
df = pd.DataFrame(
index=midx,
columns=["big", "small"],
data=[
[45, 30],
[200, 100],
[1.5, 1],
[30, 20],
[250, 150],
[1.5, 0.8],
[320, 250],
[1, 0.8],
[0.3, 0.2],
],
)
with pytest.warns(UserWarning):
df.drop(index="length", level=1)
