def delete(self,
pos: Indices2D,
axis: Optional[int] = 0,
inline: bool = False) -> StructuredArray:
narr = self if inline else self.copy()
if isstring(pos):
del narr._arrs[pos]
return narr
sids, aids = self._parseids(pos, axis=axis, mapslice=False)
slic = isinstance(sids, slice) and sids == slice(None)
alic = isinstance(aids,
slice) and aids == slice(None) and (missing(axis)
or axis == 0)
if slic and alic:
narr._arrs = OrderedDict()
narr._length = None
elif slic and not alic:
if listable(aids) and len(aids) == 1 and aids[0] < 0:
aids = aids[
0] # fix the issue that currently negative indices are ignored by np.delete
for k, v in narr._arrs.items():
narr._arrs[k] = np.delete(v, aids)
narr._length = len(narr._arrs[l(narr._arrs.keys())[0]])
elif not slic and alic:
if isinstance(sids, slice) or sids.dtype.kind not in ('S', 'U'):
sids = narr.names[sids]
for k in sids:
del narr._arrs[k]
else:
raise IndexError('unable to delete portion of the array')
return narr
def load(fname: Union[str, Path], *, mode: str = 'r',
skips: Optional[int] = None, comment: Optional[str] = None, strip: bool = False, **kwargs: Any) -> List[List[str]]:
checkInputFile(fname)
with open(fname, mode) as f:
if available(skips) and skips > 0:
for _ in range(skips): next(f)
tb = csv.reader(f, **kwargs)
if strip: tb = drop(tb, lambda x: len(x) == 0 or (len(x) == 1 and x[0].strip() == ''))
if available(comment): tb = drop(tb, lambda x: x[0].startswith(comment))
tb = l(tb)
return tb
def tolist(self) -> List:
return l(self._names.tolist())
def fields(self) -> List[Tuple[str, np.ndarray]]:
return l(self._arrs.items())
def arrays(self) -> List[np.ndarray]:
return l(self._arrs.values())
def names(self) -> np.ndarray:
return np.array(l(self._arrs.keys()))