def __init__(self, x, var_name='x', convert_dummies=True, drop_first=True):
self._var_name = var_name
self._convert_dummies = convert_dummies
self._drop_first = drop_first
if isinstance(x, PanelData):
x = x.dataframe
self._original = x
if isinstance(x, DataArray):
if x.ndim not in (2, 3):
raise ValueError('Only 2-d or 3-d DataArrays are supported')
x = x.to_pandas()
if isinstance(x, Series) and isinstance(x.index, pd.MultiIndex):
x = DataFrame(x)
elif isinstance(x, Series):
raise ValueError(
'Series can only be used with a 2-level MultiIndex')
if isinstance(x, (Panel, DataFrame)):
if isinstance(x, DataFrame):
if isinstance(x.index, pd.MultiIndex):
if len(x.index.levels) != 2:
raise ValueError('DataFrame input must have a '
'MultiIndex with 2 levels')
self._frame = x.copy()
else:
self._frame = DataFrame(
{var_name: x.T.stack(dropna=False)})
else:
self._frame = x.swapaxes(1,
2).to_frame(filter_observations=False)
elif isinstance(x, ndarray):
if not 2 <= x.ndim <= 3:
raise ValueError('2 or 3-d array required for numpy input')
if x.ndim == 2:
x = x[None, :, :]
k, t, n = x.shape
variables = [var_name] if k == 1 else [
var_name + '.{0}'.format(i) for i in range(k)
]
entities = ['entity.{0}'.format(i) for i in range(n)]
time = list(range(t))
x = x.astype(np.float64)
panel = Panel(x,
items=variables,
major_axis=time,
minor_axis=entities)
self._frame = panel.swapaxes(1,
2).to_frame(filter_observations=False)
else:
raise TypeError('Only ndarrays, DataFrames, Panels or DataArrays '
'supported.')
if convert_dummies:
self._frame = expand_categoricals(self._frame, drop_first)
self._frame = self._frame.astype(np.float64)
time_index = Series(self._frame.index.levels[1])
if not (is_numeric_dtype(time_index.dtype)
or is_datetime64_any_dtype(time_index.dtype)):
raise ValueError('The index on the time dimension must be either '
'numeric or date-like')
self._k, self._t, self._n = self.panel.shape
self._frame.index.levels[0].name = 'entity'
self._frame.index.levels[1].name = 'time'
def __init__(self, x, var_name='x', nobs=None, convert_dummies=True, drop_first=True):
if isinstance(x, IVData):
self.__dict__.update(copy.deepcopy(x.__dict__))
return
if x is None and nobs is not None:
x = np.empty((nobs, 0))
elif x is None:
raise ValueError('nobs required when x is None')
self.original = x
xndim = x.ndim
if xndim > 2:
raise ValueError(dim_err.format(var_name, xndim))
if isinstance(x, np.ndarray):
x = x.astype(dtype=np.float64)
if xndim == 1:
x.shape = (x.shape[0], -1)
self._ndarray = x.astype(np.float64)
index = list(range(x.shape[0]))
if x.shape[1] == 1:
cols = [var_name]
else:
cols = [var_name + '.{0}'.format(i) for i in range(x.shape[1])]
self._pandas = pd.DataFrame(x, index=index, columns=cols)
self._labels = {0: index, 1: cols}
elif isinstance(x, (pd.Series, pd.DataFrame)):
if isinstance(x, pd.Series):
name = var_name if not x.name else x.name
x = pd.DataFrame({name: x})
copied = False
columns = list(x.columns)
if len(set(columns)) != len(columns):
raise ValueError('DataFrame contains duplicate column names. '
'All column names must be distinct')
all_numeric = True
for col in x:
c = x[col]
if is_string_dtype(c.dtype) and \
c.map(lambda v: is_string_like(v)).all():
c = c.astype('category')
if not copied:
x = x.copy()
copied = True
x[col] = c
dt = c.dtype
all_numeric = all_numeric and is_numeric_dtype(dt)
if not (is_numeric_dtype(dt) or is_categorical_dtype(dt)):
raise ValueError('Only numeric, string or categorical '
'data permitted')
if convert_dummies:
x = expand_categoricals(x, drop_first)
self._pandas = x
self._ndarray = self._pandas.values
if all_numeric or convert_dummies:
self._ndarray = self._ndarray.astype(np.float64)
self._labels = {i: list(label) for i, label in zip(range(x.ndim), x.axes)}
else:
try:
import xarray as xr
except ImportError:
raise TypeError(type_err)
if isinstance(x, xr.DataArray):
if x.ndim == 1:
x = xr.concat([x], dim=var_name).transpose()
index = list(x.coords[x.dims[0]].values)
cols = x.coords[x.dims[1]].values
if is_numeric_dtype(cols.dtype):
cols = [var_name + '.{0}'.format(i) for i in range(x.shape[1])]
cols = list(cols)
self._ndarray = x.values.astype(np.float64)
self._pandas = pd.DataFrame(self._ndarray, columns=cols,
index=index)
self._labels = {0: index, 1: cols}
else:
raise TypeError(type_err)
if nobs is not None:
if self._ndarray.shape[0] != nobs:
msg = 'Array required to have {nobs} obs, has ' \
'{act}'.format(nobs=nobs, act=self._ndarray.shape[0])
raise ValueError(msg)
def __init__(self,
x,
var_name='x',
convert_dummies=True,
drop_first=True,
copy=True):
self._var_name = var_name
self._convert_dummies = convert_dummies
self._drop_first = drop_first
self._panel = None
self._shape = None
index_names = ['entity', 'time']
if isinstance(x, PanelData):
x = x.dataframe
self._original = x
if not isinstance(x, (Series, DataFrame, Panel, np.ndarray)):
try:
from xarray import DataArray
if isinstance(x, DataArray):
if x.ndim not in (2, 3):
raise ValueError(
'Only 2-d or 3-d DataArrays are supported')
if x.ndim == 2:
x = x.to_pandas()
else:
items = x.coords[x.dims[0]].values.tolist()
major = x.coords[x.dims[1]].values.tolist()
minor = x.coords[x.dims[2]].values.tolist()
values = x.values
x = panel_to_frame(values, items, major, minor, True)
except ImportError:
pass
if isinstance(x, Series) and isinstance(x.index, MultiIndex):
x = DataFrame(x)
elif isinstance(x, Series):
raise ValueError(
'Series can only be used with a 2-level MultiIndex')
if isinstance(x, (Panel, DataFrame)):
if isinstance(x, DataFrame):
if isinstance(x.index, MultiIndex):
if len(x.index.levels) != 2:
raise ValueError('DataFrame input must have a '
'MultiIndex with 2 levels')
if isinstance(self._original,
(DataFrame, PanelData, Series)):
for i in range(2):
index_names[
i] = x.index.levels[i].name or index_names[i]
self._frame = x
if copy:
self._frame = self._frame.copy()
else:
self._frame = DataFrame(
{var_name: x.T.stack(dropna=False)})
else:
self._frame = x.swapaxes(1,
2).to_frame(filter_observations=False)
elif isinstance(x, np.ndarray):
if x.ndim not in (2, 3):
raise ValueError('2 or 3-d array required for numpy input')
if x.ndim == 2:
x = x[None, :, :]
k, t, n = x.shape
var_str = var_name + '.{0:0>' + str(int(np.log10(k) + .01)) + '}'
variables = [var_name] if k == 1 else [
var_str.format(i) for i in range(k)
]
entity_str = 'entity.{0:0>' + str(int(np.log10(n) + .01)) + '}'
entities = [entity_str.format(i) for i in range(n)]
time = list(range(t))
x = x.astype(np.float64, copy=False)
panel = _Panel.from_array(x,
items=variables,
major_axis=time,
minor_axis=entities)
self._fake_panel = panel
self._frame = panel.to_frame()
else:
raise TypeError('Only ndarrays, DataFrames, Panels or DataArrays '
'are supported')
if convert_dummies:
self._frame = expand_categoricals(self._frame, drop_first)
self._frame = self._frame.astype(np.float64, copy=False)
time_index = Series(self._frame.index.levels[1])
if not (is_numeric_dtype(time_index.dtype)
or is_datetime64_any_dtype(time_index.dtype)):
raise ValueError('The index on the time dimension must be either '
'numeric or date-like')
# self._k, self._t, self._n = self.panel.shape
self._k, self._t, self._n = self.shape
levels = self._frame.index.levels
for i in range(2):
levels[i].name = index_names[i]