def get_page(self, start: int, end: int) -> List[List[Any]]:
ret = []
columns = self._table.columns
for i in self._index[start:end]:
row = [i]
for name in columns:
col = self._table[name]
row.append(remove_nan(col.loc[i]))
ret.append(row)
return ret
def to_csv(self, filename, columns=None, sep=','): # TODO: to be improved
if columns is None:
columns = self.columns
with open(filename, 'wb') as f:
for i in self.index:
row = []
for name in columns:
col = self[name]
row.append(str(remove_nan(get_physical_base(col).loc[i])))
row = sep.join(row)
f.write(row.encode('utf-8'))
f.write(b'\n')
def to_csv(self,
filename: str,
columns: Optional[List[str]] = None,
sep: str = ",") -> None: # TODO: to be improved
if columns is None:
columns = self.columns
with open(filename, "wb") as f:
for i in self.index:
row = []
for name in columns:
col = self[name]
row.append(str(remove_nan(get_physical_base(col).loc[i])))
f.write(sep.join(row).encode("utf-8"))
f.write(b"\n")
def remove_nan(d):
return remove_nan(d)
def to_dict(self,
orient: str = "dict",
columns: Optional[List[str]] = None) -> Any:
# pylint: disable=too-many-branches
"""
Return a dictionary describing the contents of this columns.
Parameters
----------
orient : {'dict', 'list', 'split', 'rows', 'datatable', 'records', 'index'}
TODO
columns : list or `None`
TODO
"""
ret: Dict[str, Any]
ret2: List[Any]
if columns is None:
columns = self.columns
if orient == "dict":
ret = dict()
for name in columns:
col = self[name]
ret[name] = {
int(k): v
for (k, v) in dict(zip(self.index, col.tolist())).items()
} # because a custom JSONEncoder cannot fix it
return ret
if orient == "list":
ret = dict()
for name in columns:
col = self[name]
ret[name] = col.tolist()
return ret
if orient == "split":
ret = {"index": list(self.index), "columns": columns}
data = []
cols = [self[c] for c in columns]
for i in self.index:
line = []
for col in cols:
line.append(get_physical_base(col).loc[i])
data.append(line)
ret["data"] = data
return ret
if orient == "datatable":
# not a pandas compliant mode but useful for JS DataTable
ret2 = []
for i in self.index:
line = [i]
for name in columns:
col = self[name]
line.append(remove_nan(get_physical_base(col).loc[i]))
ret2.append(line)
return ret2
if orient in ("rows", "records"):
ret2 = []
for i in self.index:
line2 = {}
for name in columns:
col = self[name]
line2[name] = get_physical_base(col).loc[i]
ret2.append(line2)
return ret2
if orient == "index":
ret3: Dict[int, Any] = dict()
for id_ in self.index:
line2 = {}
for name in columns:
col = self[name]
line2[name] = col.loc[id_]
ret3[int(id_)] = line2
return ret3
raise ValueError(f"to_dict({orient}) not implemented")
def to_dict(self, orient='dict', columns=None):
# pylint: disable=too-many-branches
"""
Return a dictionary describing the contents of this columns.
Parameters
----------
orient : {'dict', 'list', 'split', 'rows', 'record', 'index'}
TODO
columns : list or `None`
TODO
"""
if columns is None:
columns = self.columns
if orient == 'dict':
ret = OrderedDict()
for name in columns:
col = self[name]
ret[name] = dict(zip(self.index, col.tolist()))
return ret
if orient == 'list':
ret = OrderedDict()
for name in columns:
col = self[name]
ret[name] = col.tolist()
return ret
if orient == 'split':
ret = {'index': self.index.tolist(), 'columns': columns}
data = []
cols = [self[c] for c in columns]
for i in self.index:
line = []
for col in cols:
#col_i = col[i]
#if isinstance(col_i, np.ndarray):
# col_i = col_i.tolist()
line.append(get_physical_base(col).loc[i])
data.append(line)
ret['data'] = data
return ret
#if orient == 'rows':
# data = []
# cols = [self[c] for c in columns]
# for i in self.index:
# line = [i]
# for col in cols:
# line.append(str(col[i]))
# data.append(dict(zip(columns, line)))
# return data
if orient == 'datatable': # not a pandas compliant mode but useful for JS DataTable
ret = []
for i in self.index:
line = [i]
for name in columns:
col = self[name]
line.append(remove_nan(get_physical_base(col).loc[i]))
ret.append(line)
return ret
if orient in ('rows', 'records'):
ret = []
for i in self.index:
line = OrderedDict()
for name in columns:
col = self[name]
#line[name] = remove_nan_etc(col.values[i])
line[name] = get_physical_base(col).loc[i]
ret.append(line)
return ret
if orient == 'index':
ret = OrderedDict()
for id_ in self.index:
line = {}
for name in columns:
col = self[name]
#line[name] = remove_nan_etc(col.values[i])
line[name] = col.loc[id_]
ret[int(id_)] = line
return ret
raise ValueError(
"to_dict(orient) not implemented for orient={}".format(orient))