def test_df_eq():
df1 = ArrayDataFrame([[0, 100.0, "a"]], "a:int,b:double,c:str", dict(a=1))
df2 = ArrayDataFrame([[0, 100.001, "a"]], "a:int,b:double,c:str",
dict(a=2))
assert df_eq(df1, df1)
assert df_eq(df1, df2, digits=4, check_metadata=False)
# metadata
assert not df_eq(df1, df2, digits=4, check_metadata=True)
# precision
assert not df_eq(df1, df2, digits=6, check_metadata=False)
# no content
assert df_eq(df1, df2, digits=6, check_metadata=False, check_content=False)
raises(AssertionError, lambda: df_eq(df1, df2, throw=True))
df1 = ArrayDataFrame([[100.0, "a"]], "a:double,b:str", dict(a=1))
assert df_eq(df1, df1.as_pandas(), df1.schema, df1.metadata)
df1 = ArrayDataFrame([[None, "a"]], "a:double,b:str", dict(a=1))
assert df_eq(df1, df1)
df1 = ArrayDataFrame([[None, "a"]], "a:double,b:str", dict(a=1))
df2 = ArrayDataFrame([[np.nan, "a"]], "a:double,b:str", dict(a=1))
assert df_eq(df1, df2)
df1 = ArrayDataFrame([[100.0, None]], "a:double,b:str", dict(a=1))
df2 = ArrayDataFrame([[100.0, None]], "a:double,b:str", dict(a=1))
assert df_eq(df1, df2)
df1 = ArrayDataFrame([[0], [1]], "a:int")
df2 = ArrayDataFrame([[1], [0]], "a:int")
assert df_eq(df1, df2)
assert not df_eq(df1, df2, check_order=True)
def test_nan_none():
df = ArrayDataFrame([[None, None]], "b:str,c:double")
assert df.as_pandas().iloc[0, 0] is None
arr = PandasDataFrame(df.as_pandas(), df.schema).as_array()[0]
assert arr[0] is None
assert math.isnan(arr[1])
df = ArrayDataFrame([[None, None]], "b:int,c:bool")
arr = PandasDataFrame(df.as_pandas(),
df.schema).as_array(type_safe=True)[0]
assert arr[0] is None
assert arr[1] is None
df = ArrayDataFrame([["a", 1.1], [None, None]], "b:str,c:double")
arr = PandasDataFrame(df.as_pandas(),
df.schema).as_array(type_safe=True)[1]
assert arr[0] is None
assert arr[1] is None
def test_nan_none():
# TODO: on dask, these tests can't pass
# df = ArrayDataFrame([[None, None]], "b:str,c:double")
# assert df.as_pandas().iloc[0, 0] is None
# arr = DaskDataFrame(df.as_pandas(), df.schema).as_array()[0]
# assert arr[0] is None
# assert math.isnan(arr[1])
# df = ArrayDataFrame([[None, None]], "b:int,c:bool")
# arr = DaskDataFrame(df.as_pandas(), df.schema).as_array(type_safe=True)[0]
# assert np.isnan(arr[0]) # TODO: this will cause inconsistent behavior cross engine
# assert np.isnan(arr[1]) # TODO: this will cause inconsistent behavior cross engine
df = ArrayDataFrame([["a", 1.1], [None, None]], "b:str,c:double")
arr = DaskDataFrame(df.as_pandas(), df.schema).as_array()[1]
assert arr[0] is None
assert math.isnan(arr[1])
arr = DaskDataFrame(df.as_array(), df.schema).as_array()[1]
assert arr[0] is None
assert math.isnan(arr[1])
arr = DaskDataFrame(df.as_pandas()["b"], "b:str").as_array()[1]
assert arr[0] is None
def test_to_local_df():
df = ArrayDataFrame([[0, 1]], "a:int,b:int")
pdf = PandasDataFrame(df.as_pandas(), "a:int,b:int")
idf = IterableDataFrame([[0, 1]], "a:int,b:int")
assert to_local_df(df) is df
assert to_local_df(pdf) is pdf
assert to_local_df(idf) is idf
assert isinstance(to_local_df(df.native, "a:int,b:int"), ArrayDataFrame)
assert isinstance(to_local_df(pdf.native, "a:int,b:int"), PandasDataFrame)
assert isinstance(to_local_df(idf.native, "a:int,b:int"),
IterableDataFrame)
raises(TypeError, lambda: to_local_df(123))
metadata = dict(a=1)
assert to_local_df(df.native, df.schema, metadata).metadata == metadata
raises(NoneArgumentError, lambda: to_local_df(None))
raises(ValueError, lambda: to_local_df(df, "a:int,b:int", None))
