def validate_ordered(ordered: Ordered) -> None:
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
def __init__(
self,
objs: Iterable[NDFrame] | Mapping[Hashable, NDFrame],
axis=0,
join: str = "outer",
keys=None,
levels=None,
names=None,
ignore_index: bool = False,
verify_integrity: bool = False,
copy: bool = True,
sort=False,
):
if isinstance(objs, (ABCSeries, ABCDataFrame, str)):
raise TypeError(
"first argument must be an iterable of pandas "
f'objects, you passed an object of type "{type(objs).__name__}"'
)
if join == "outer":
self.intersect = False
elif join == "inner":
self.intersect = True
else: # pragma: no cover
raise ValueError(
"Only can inner (intersect) or outer (union) join the other axis"
)
if isinstance(objs, abc.Mapping):
if keys is None:
keys = list(objs.keys())
objs = [objs[k] for k in keys]
else:
objs = list(objs)
if len(objs) == 0:
raise ValueError("No objects to concatenate")
if keys is None:
objs = list(com.not_none(*objs))
else:
# #1649
clean_keys = []
clean_objs = []
for k, v in zip(keys, objs):
if v is None:
continue
clean_keys.append(k)
clean_objs.append(v)
objs = clean_objs
if isinstance(keys, MultiIndex):
# TODO: retain levels?
keys = type(keys).from_tuples(clean_keys, names=keys.names)
else:
name = getattr(keys, "name", None)
keys = Index(clean_keys, name=name)
if len(objs) == 0:
raise ValueError("All objects passed were None")
# figure out what our result ndim is going to be
ndims = set()
for obj in objs:
if not isinstance(obj, (ABCSeries, ABCDataFrame)):
msg = (f"cannot concatenate object of type '{type(obj)}'; "
"only Series and DataFrame objs are valid")
raise TypeError(msg)
ndims.add(obj.ndim)
# get the sample
# want the highest ndim that we have, and must be non-empty
# unless all objs are empty
sample: NDFrame | None = None
if len(ndims) > 1:
max_ndim = max(ndims)
for obj in objs:
if obj.ndim == max_ndim and np.sum(obj.shape):
sample = obj
break
else:
# filter out the empties if we have not multi-index possibilities
# note to keep empty Series as it affect to result columns / name
non_empties = [
obj for obj in objs
if sum(obj.shape) > 0 or isinstance(obj, ABCSeries)
]
if len(non_empties) and (keys is None and names is None and
levels is None and not self.intersect):
objs = non_empties
sample = objs[0]
if sample is None:
sample = objs[0]
self.objs = objs
# Standardize axis parameter to int
if isinstance(sample, ABCSeries):
from pandas import DataFrame
axis = DataFrame._get_axis_number(axis)
else:
axis = sample._get_axis_number(axis)
# Need to flip BlockManager axis in the DataFrame special case
self._is_frame = isinstance(sample, ABCDataFrame)
if self._is_frame:
axis = sample._get_block_manager_axis(axis)
self._is_series = isinstance(sample, ABCSeries)
if not 0 <= axis <= sample.ndim:
raise AssertionError(
f"axis must be between 0 and {sample.ndim}, input was {axis}")
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
if len(ndims) > 1:
current_column = 0
max_ndim = sample.ndim
self.objs, objs = [], self.objs
for obj in objs:
ndim = obj.ndim
if ndim == max_ndim:
pass
elif ndim != max_ndim - 1:
raise ValueError("cannot concatenate unaligned mixed "
"dimensional NDFrame objects")
else:
name = getattr(obj, "name", None)
if ignore_index or name is None:
name = current_column
current_column += 1
# doing a row-wise concatenation so need everything
# to line up
if self._is_frame and axis == 1:
name = 0
# mypy needs to know sample is not an NDFrame
sample = cast("DataFrame | Series", sample)
obj = sample._constructor({name: obj})
self.objs.append(obj)
# note: this is the BlockManager axis (since DataFrame is transposed)
self.bm_axis = axis
self.axis = 1 - self.bm_axis if self._is_frame else 0
self.keys = keys
self.names = names or getattr(keys, "names", None)
self.levels = levels
if not is_bool(sort):
warnings.warn(
"Passing non boolean values for sort is deprecated and "
"will error in a future version!",
FutureWarning,
stacklevel=find_stack_level(),
)
self.sort = sort
self.ignore_index = ignore_index
self.verify_integrity = verify_integrity
self.copy = copy
self.new_axes = self._get_new_axes()
