def _init_data(self, data, copy, dtype, **kwargs): """ Generate ND initialization; axes are passed as required objects to __init__ """ if data is None: data = {} if dtype is not None: dtype = self._validate_dtype(dtype) passed_axes = [kwargs.get(a) for a in self._AXIS_ORDERS] axes = None if isinstance(data, BlockManager): if any(x is not None for x in passed_axes): axes = [x if x is not None else y for x, y in zip(passed_axes, data.axes)] mgr = data elif isinstance(data, dict): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None else: # pragma: no cover raise PandasError('Panel constructor not properly called!') NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def _init_data(self, data, copy, dtype, **kwargs): """ Generate ND initialization; axes are passed as required objects to __init__ """ if data is None: data = {} if dtype is not None: dtype = self._validate_dtype(dtype) passed_axes = [kwargs.get(a) for a in self._AXIS_ORDERS] axes = None if isinstance(data, BlockManager): if any(x is not None for x in passed_axes): axes = [ x if x is not None else y for x, y in zip(passed_axes, data.axes) ] mgr = data elif isinstance(data, dict): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None else: # pragma: no cover raise PandasError('Panel constructor not properly called!') NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def __init__(self, data=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None): """ Represents wide format panel data, stored as 3-dimensional array Parameters ---------- data : ndarray (items x major x minor), or dict of DataFrames items : Index or array-like axis=1 major_axis : Index or array-like axis=1 minor_axis : Index or array-like axis=2 dtype : dtype, default None Data type to force, otherwise infer copy : boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input """ if data is None: data = {} passed_axes = [items, major_axis, minor_axis] axes = None if isinstance(data, BlockManager): if any(x is not None for x in passed_axes): axes = [ x if x is not None else y for x, y in zip(passed_axes, data.axes) ] mgr = data elif isinstance(data, dict): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None else: # pragma: no cover raise PandasError('Panel constructor not properly called!') NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def __init__(self, data=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None): """ Represents wide format panel data, stored as 3-dimensional array Parameters ---------- data : ndarray (items x major x minor), or dict of DataFrames items : Index or array-like axis=1 major_axis : Index or array-like axis=1 minor_axis : Index or array-like axis=2 dtype : dtype, default None Data type to force, otherwise infer copy : boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input """ if data is None: data = {} passed_axes = [items, major_axis, minor_axis] axes = None if isinstance(data, BlockManager): if any(x is not None for x in passed_axes): axes = [x if x is not None else y for x, y in zip(passed_axes, data.axes)] mgr = data elif isinstance(data, dict): mgr = self._init_dict(data, passed_axes, dtype=dtype) copy = False dtype = None elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy) copy = False dtype = None else: # pragma: no cover raise PandasError('Panel constructor not properly called!') NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)
def __init__(self, data=None, index=None, columns=None, default_kind=None, default_fill_value=None, dtype=None, copy=False): # pick up the defaults from the Sparse structures if isinstance(data, SparseDataFrame): if index is None: index = data.index if columns is None: columns = data.columns if default_fill_value is None: default_fill_value = data.default_fill_value if default_kind is None: default_kind = data.default_kind elif isinstance(data, (SparseSeries, SparseArray)): if index is None: index = data.index if default_fill_value is None: default_fill_value = data.fill_value if columns is None and hasattr(data, 'name'): columns = [data.name] if columns is None: raise Exception("cannot pass a series w/o a name or columns") data = {columns[0]: data} if default_fill_value is None: default_fill_value = np.nan if default_kind is None: default_kind = 'block' self._default_kind = default_kind self._default_fill_value = default_fill_value if isinstance(data, dict): mgr = self._init_dict(data, index, columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, index, columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, SparseDataFrame): mgr = self._init_mgr( data._data, dict(index=index, columns=columns), dtype=dtype, copy=copy) elif isinstance(data, DataFrame): mgr = self._init_dict(data, data.index, data.columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, BlockManager): mgr = self._init_mgr( data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) elif data is None: data = {} if index is None: index = Index([]) else: index = _ensure_index(index) if columns is None: columns = Index([]) else: for c in columns: data[c] = SparseArray(np.nan, index=index, kind=self._default_kind, fill_value=self._default_fill_value) mgr = dict_to_manager(data, columns, index) if dtype is not None: mgr = mgr.astype(dtype) NDFrame.__init__(self, mgr)
def __init__(self, data=None, index=None, columns=None, default_kind=None, default_fill_value=None, dtype=None, copy=False): # pick up the defaults from the Sparse structures if isinstance(data, SparseDataFrame): if index is None: index = data.index if columns is None: columns = data.columns if default_fill_value is None: default_fill_value = data.default_fill_value if default_kind is None: default_kind = data.default_kind elif isinstance(data, (SparseSeries, SparseArray)): if index is None: index = data.index if default_fill_value is None: default_fill_value = data.fill_value if columns is None and hasattr(data, 'name'): columns = [data.name] if columns is None: raise Exception("cannot pass a series w/o a name or columns") data = {columns[0]: data} if default_fill_value is None: default_fill_value = np.nan if default_kind is None: default_kind = 'block' self._default_kind = default_kind self._default_fill_value = default_fill_value if isinstance(data, dict): mgr = self._init_dict(data, index, columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, (np.ndarray, list)): mgr = self._init_matrix(data, index, columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, SparseDataFrame): mgr = self._init_mgr(data._data, dict(index=index, columns=columns), dtype=dtype, copy=copy) elif isinstance(data, DataFrame): mgr = self._init_dict(data, data.index, data.columns) if dtype is not None: mgr = mgr.astype(dtype) elif isinstance(data, BlockManager): mgr = self._init_mgr(data, axes=dict(index=index, columns=columns), dtype=dtype, copy=copy) elif data is None: data = {} if index is None: index = Index([]) else: index = _ensure_index(index) if columns is None: columns = Index([]) else: for c in columns: data[c] = SparseArray(np.nan, index=index, kind=self._default_kind, fill_value=self._default_fill_value) mgr = dict_to_manager(data, columns, index) if dtype is not None: mgr = mgr.astype(dtype) NDFrame.__init__(self, mgr)