def assign(
self, py_obj = None
):
if is_numpy_array(py_obj) or isinstance(py_obj, list):
py_type = self.from_obj(py_obj)
return self.assign_type(py_type)
return InvalidType()
def assign(
self,
py_obj: t.Optional[t.Any] = None
) -> t.Union["NDArrayType", InvalidType]:
if is_numpy_array(py_obj) or isinstance(py_obj, list):
py_type = self.from_obj(py_obj)
return self.assign_type(py_type)
return InvalidType()
def _init_from_ndarray(self, ndarray, columns, optional=True, dtype=None):
assert util.is_numpy_array(
ndarray), "ndarray argument expects a `numpy.ndarray` object"
self.data = []
self._assert_valid_columns(columns)
self.columns = columns
self._make_column_types(dtype, optional)
for row in ndarray.tolist():
self.add_data(*row)
def numpy_arrays_to_lists(payload):
"""Casts all numpy arrays to lists so we don't convert them to histograms, primarily for Plotly
"""
for key, val in six.iteritems(payload):
if isinstance(val, dict):
payload[key] = numpy_arrays_to_lists(val)
elif util.is_numpy_array(val):
payload[key] = val.tolist()
return payload
def from_obj(cls, py_obj: t.Optional[t.Any] = None) -> "NDArrayType":
if is_numpy_array(py_obj):
return cls(py_obj.shape) # type: ignore
elif isinstance(py_obj, list):
shape = []
target = py_obj
while isinstance(target, list):
dim = len(target)
shape.append(dim)
if dim > 0:
target = target[0]
return cls(shape)
else:
raise TypeError(
"NDArrayType.from_obj expects py_obj to be ndarray or list, found {}"
.format(py_obj.__class__))
def __init__(
self,
columns=None,
data=None,
rows=None,
dataframe=None,
dtype=None,
optional=True,
allow_mixed_types=False,
):
"""rows is kept for legacy reasons, we use data to mimic the Pandas api"""
super(Table, self).__init__()
if allow_mixed_types:
dtype = _dtypes.AnyType
# This is kept for legacy reasons (tss: personally, I think we should remove this)
if columns is None:
columns = ["Input", "Output", "Expected"]
# Explicit dataframe option
if dataframe is not None:
self._init_from_dataframe(dataframe, columns, optional, dtype)
else:
# Expected pattern
if data is not None:
if util.is_numpy_array(data):
self._init_from_ndarray(data, columns, optional, dtype)
elif util.is_pandas_data_frame(data):
self._init_from_dataframe(data, columns, optional, dtype)
else:
self._init_from_list(data, columns, optional, dtype)
# legacy
elif rows is not None:
self._init_from_list(rows, columns, optional, dtype)
# Default empty case
else:
self._init_from_list([], columns, optional, dtype)