def wrap(self, a, index=None, columns=None, dtype=None, group_by=None):
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
_self = self.resolve(group_by=group_by)
a = np.asarray(a)
a = reshape_fns.soft_to_ndim(a, self.ndim)
if index is None:
index = _self.index
if columns is None:
columns = _self.columns
if columns is not None and len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
if index is not None:
checks.assert_shape_equal(a, index, axis=(0, 0))
if a.ndim == 2 and columns is not None:
checks.assert_shape_equal(a, columns, axis=(1, 0))
if a.ndim == 1:
return pd.Series(a, index=index, name=name, dtype=dtype)
if a.ndim == 2:
if a.shape[1] == 1 and _self.ndim == 1:
return pd.Series(a[:, 0], index=index, name=name, dtype=dtype)
return pd.DataFrame(a, index=index, columns=columns, dtype=dtype)
raise ValueError(f"{a.ndim}-d input is not supported")
def wrap(self, a, index=None, columns=None, ndim=None, dtype=None):
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
a = np.asarray(a)
if ndim is None:
ndim = self.ndim
if ndim is not None:
a = reshape_fns.soft_to_ndim(a, self.ndim)
if index is None:
index = self.index
if columns is None:
columns = self.columns
if columns is not None and len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
# Perform checks
if index is not None:
checks.assert_same_shape(a, index, axis=(0, 0))
if a.ndim == 2 and columns is not None:
checks.assert_same_shape(a, columns, axis=(1, 0))
if a.ndim == 1:
return pd.Series(a, index=index, name=name, dtype=dtype)
return pd.DataFrame(a, index=index, columns=columns, dtype=dtype)
def _wrap_reduced(arr):
nonlocal name_or_index
arr = np.asarray(arr)
if fillna is not None:
arr[pd.isnull(arr)] = fillna
if arr.ndim == 0:
# Scalar per Series/DataFrame
return pd.Series(arr, dtype=dtype)[0]
if arr.ndim == 1:
if _self.ndim == 1:
if arr.shape[0] == 1:
# Scalar per Series/DataFrame with one column
return pd.Series(arr, dtype=dtype)[0]
# Array per Series
sr_name = columns[0]
if sr_name == 0: # was arr Series before
sr_name = None
if isinstance(name_or_index, str):
name_or_index = None
return pd.Series(arr,
index=name_or_index,
name=sr_name,
dtype=dtype)
# Scalar per column in arr DataFrame
return pd.Series(arr,
index=columns,
name=name_or_index,
dtype=dtype)
if arr.ndim == 2:
if arr.shape[1] == 1 and _self.ndim == 1:
arr = reshape_fns.soft_to_ndim(arr, 1)
# Array per Series
sr_name = columns[0]
if sr_name == 0: # was arr Series before
sr_name = None
if isinstance(name_or_index, str):
name_or_index = None
return pd.Series(arr,
index=name_or_index,
name=sr_name,
dtype=dtype)
# Array per column in DataFrame
if isinstance(name_or_index, str):
name_or_index = None
return pd.DataFrame(arr,
index=name_or_index,
columns=columns,
dtype=dtype)
raise ValueError(f"{arr.ndim}-d input is not supported")
def wrap(self,
a: tp.ArrayLike,
index: tp.Optional[tp.IndexLike] = None,
columns: tp.Optional[tp.IndexLike] = None,
dtype: tp.Optional[tp.PandasDTypeLike] = None,
group_by: tp.GroupByLike = None) -> tp.SeriesFrame:
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
_self = self.resolve(group_by=group_by)
if index is None:
index = _self.index
if not isinstance(index, pd.Index):
index = pd.Index(index)
if columns is None:
columns = _self.columns
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
if len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
arr = np.asarray(a)
arr = reshape_fns.soft_to_ndim(arr, self.ndim)
checks.assert_shape_equal(arr, index, axis=(0, 0))
if arr.ndim == 2:
checks.assert_shape_equal(arr, columns, axis=(1, 0))
if arr.ndim == 1:
return pd.Series(arr, index=index, name=name, dtype=dtype)
if arr.ndim == 2:
if arr.shape[1] == 1 and _self.ndim == 1:
return pd.Series(arr[:, 0],
index=index,
name=name,
dtype=dtype)
return pd.DataFrame(arr, index=index, columns=columns, dtype=dtype)
raise ValueError(f"{arr.ndim}-d input is not supported")
def _wrap(arr):
arr = np.asarray(arr)
checks.assert_ndim(arr, (1, 2))
if fillna is not None:
arr[pd.isnull(arr)] = fillna
arr = reshape_fns.soft_to_ndim(arr, self.ndim)
checks.assert_shape_equal(arr, index, axis=(0, 0))
if arr.ndim == 2:
checks.assert_shape_equal(arr, columns, axis=(1, 0))
if arr.ndim == 1:
return pd.Series(arr, index=index, name=name, dtype=dtype)
if arr.ndim == 2:
if arr.shape[1] == 1 and _self.ndim == 1:
return pd.Series(arr[:, 0],
index=index,
name=name,
dtype=dtype)
return pd.DataFrame(arr,
index=index,
columns=columns,
dtype=dtype)
raise ValueError(f"{arr.ndim}-d input is not supported")
def wrap(self,
a,
index=None,
columns=None,
dtype=None,
collapse=None,
**kwargs):
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
group_by = self.grouper.resolve_group_by(**kwargs)
a = np.asarray(a)
a = reshape_fns.soft_to_ndim(a, self.ndim)
if index is None:
index = self.index
if columns is None:
columns = self.grouper.get_columns(**kwargs)
if collapse is None:
collapse = group_by is not None and group_by is not False and self.grouped_ndim == 1
if columns is not None and len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
# Perform checks
if index is not None:
checks.assert_shape_equal(a, index, axis=(0, 0))
if a.ndim == 2 and columns is not None:
checks.assert_shape_equal(a, columns, axis=(1, 0))
if a.ndim == 1:
return pd.Series(a, index=index, name=name, dtype=dtype)
if a.ndim == 2 and a.shape[1] == 1 and collapse:
return pd.Series(a[:, 0], index=index, name=name, dtype=dtype)
return pd.DataFrame(a, index=index, columns=columns, dtype=dtype)