def test_destructors() -> None:
def gen_values() -> Iterator[Dict[str, np.ndarray]]:
yield {'x': np.arange(6)}
yield {'y': np.arange(6)}
t = Tafra(gen_values())
check_tafra(t)
t = build_tafra()
t = t.update_dtypes({'x': 'float'})
t.data['x'][2] = np.nan
check_tafra(t)
_ = tuple(t.to_records())
_ = tuple(t.to_records(columns='x'))
_ = tuple(t.to_records(columns=['x']))
_ = tuple(t.to_records(columns=['x', 'y']))
_ = tuple(t.to_records(cast_null=False))
_ = tuple(t.to_records(columns='x', cast_null=False))
_ = tuple(t.to_records(columns=['x'], cast_null=False))
_ = tuple(t.to_records(columns=['x', 'y'], cast_null=False))
_ = t.to_list()
_ = t.to_list(columns='x')
_ = t.to_list(columns=['x'])
_ = t.to_list(columns=['x', 'y'])
_ = t.to_list(inner=True)
_ = t.to_list(columns='x', inner=True)
_ = t.to_list(columns=['x'], inner=True)
_ = t.to_list(columns=['x', 'y'], inner=True)
_ = t.to_tuple()
_ = t.to_tuple(columns='x')
_ = t.to_tuple(columns=['x'])
_ = t.to_tuple(columns=['x', 'y'])
_ = t.to_tuple(inner=True)
_ = t.to_tuple(columns='x', inner=True)
_ = t.to_tuple(columns=['x'], inner=True)
_ = t.to_tuple(columns=['x', 'y'], inner=True)
_ = t.to_array()
_ = t.to_array(columns='x')
_ = t.to_array(columns=['x'])
_ = t.to_array(columns=['x', 'y'])
_ = t.to_pandas()
_ = t.to_pandas(columns='x')
_ = t.to_pandas(columns=['x'])
_ = t.to_pandas(columns=['x', 'y'])
filepath = Path('test/test_to_csv.csv')
t.to_csv(filepath)
t.to_csv(filepath, columns='x')
t.to_csv(filepath, columns=['x'])
t.to_csv(filepath, columns=['x', 'y'])
def check_tafra(t: Tafra, check_rows: bool = True) -> bool:
assert len(t._data) == len(t._dtypes)
for c in t.columns:
assert isinstance(t[c], np.ndarray)
assert isinstance(t.data[c], np.ndarray)
assert isinstance(t._data[c], np.ndarray)
assert isinstance(t.dtypes[c], str)
assert isinstance(t._dtypes[c], str)
if check_rows:
assert t._rows == len(t._data[c])
pd.Series(t._data[c])
columns = [c for c in t.columns][:-1]
_ = t.to_records()
_ = t.to_records(columns=columns)
_ = t.to_list()
_ = t.to_list(columns=columns)
_ = t.to_list(inner=True)
_ = t.to_list(columns=columns, inner=True)
_ = t.to_tuple()
_ = t.to_tuple(columns=columns)
_ = t.to_tuple(name=None)
_ = t.to_tuple(name='tf')
_ = t.to_tuple(columns=columns, name=None)
_ = t.to_tuple(columns=columns, name='tf')
_ = t.to_tuple(inner=True)
_ = t.to_tuple(inner=True, name=None)
_ = t.to_tuple(inner=True, name='tf')
_ = t.to_tuple(columns=columns, inner=True)
_ = t.to_tuple(columns=columns, inner=True, name=None)
_ = t.to_tuple(columns=columns, inner=True, name='tf')
_ = t.to_array()
_ = t.to_array(columns=columns)
df = t.to_pandas()
df = t.to_pandas(columns=columns)
assert isinstance(df, pd.DataFrame)
write_path = Path('test/test_to_csv.csv')
t.to_csv(write_path)
# t.to_csv(write_path, columns=columns)
return True
def check_tafra(t: Tafra) -> bool:
assert len(t._data) == len(t._dtypes)
for c in t.columns:
assert isinstance(t[c], np.ndarray)
assert isinstance(t.data[c], np.ndarray)
assert isinstance(t._data[c], np.ndarray)
assert isinstance(t.dtypes[c], str)
assert isinstance(t._dtypes[c], str)
assert t._rows == len(t._data[c])
pd.Series(t._data[c])
_ = t.to_records()
_ = t.to_list()
_ = t.to_list()
pd.DataFrame(t._data)
return True
def test_constructions() -> None:
with pytest.raises(TypeError) as e:
t = Tafra() # type: ignore # noqa
with pytest.raises(ValueError) as e:
t = Tafra({}) # type: ignore
t = Tafra({'x': None})
check_tafra(t)
t = Tafra({'x': Decimal('1.23456')})
check_tafra(t)
t = Tafra({'x': np.array(1)})
check_tafra(t)
t = Tafra({'x': np.array([1])})
check_tafra(t)
t = Tafra({'x': [True, False]})
check_tafra(t)
t = Tafra({'x': 'test'})
check_tafra(t)
t.update_dtypes_inplace({'x': 'O'})
check_tafra(t)
t = Tafra(enumerate(np.arange(6)))
check_tafra(t)
with pytest.raises(ValueError) as e:
t = Tafra({'x': np.array([1, 2]), 'y': np.array([3., 4., 5.])})
def gen_values() -> Iterator[Dict[str, np.ndarray]]:
yield {'x': np.arange(6)}
yield {'y': np.arange(6)}
t = Tafra(gen_values())
check_tafra(t)
t = build_tafra()
t = t.update_dtypes({'x': 'float'})
t.data['x'][2] = np.nan
check_tafra(t)
_ = tuple(t.to_records())
_ = tuple(t.to_records(columns='x'))
_ = tuple(t.to_records(columns=['x']))
_ = tuple(t.to_records(columns=['x', 'y']))
_ = tuple(t.to_records(cast_null=False))
_ = tuple(t.to_records(columns='x', cast_null=False))
_ = tuple(t.to_records(columns=['x'], cast_null=False))
_ = tuple(t.to_records(columns=['x', 'y'], cast_null=False))
_ = t.to_list()
_ = t.to_list(columns='x')
_ = t.to_list(columns=['x'])
_ = t.to_list(columns=['x', 'y'])
_ = t.to_list(inner=True)
_ = t.to_list(columns='x', inner=True)
_ = t.to_list(columns=['x'], inner=True)
_ = t.to_list(columns=['x', 'y'], inner=True)
_ = t.to_array()
_ = t.to_array(columns='x')
_ = t.to_array(columns=['x'])
_ = t.to_array(columns=['x', 'y'])
t = build_tafra()
df = pd.DataFrame(t.data)
_ = Tafra.from_series(df['x'])
check_tafra(_)
_ = Tafra.from_dataframe(df)
check_tafra(_)
_ = Tafra.as_tafra(df)
check_tafra(_)
_ = Tafra.as_tafra(df['x'])
check_tafra(_)
_ = Tafra.as_tafra(t)
check_tafra(_)
_ = Tafra.as_tafra({'x': np.array(1)})
check_tafra(_)
_ = Tafra.from_series(Series())
check_tafra(_)
_ = Tafra.as_tafra(Series())
check_tafra(_)
_ = Tafra.from_dataframe(DataFrame()) # type: ignore
check_tafra(_)
_ = Tafra.as_tafra(DataFrame())
check_tafra(_)
with pytest.raises(TypeError) as e:
_ = Tafra(np.arange(6))
with pytest.raises(TypeError) as e:
_ = Tafra.as_tafra(np.arange(6))