def _init_matrix(self, data, index, columns, dtype=None):
data = _prep_ndarray(data, copy=False)
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError("Column length mismatch: %d vs. %d" % (len(columns), K))
if len(index) != N:
raise ValueError("Index length mismatch: %d vs. %d" % (len(index), N))
data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
return self._init_dict(data, index, columns, dtype)
def _init_matrix(self, data, index, columns, dtype=None):
data = _prep_ndarray(data, copy=False)
N, K = data.shape
if index is None:
index = _default_index(N)
if columns is None:
columns = _default_index(K)
if len(columns) != K:
raise ValueError('Column length mismatch: %d vs. %d' %
(len(columns), K))
if len(index) != N:
raise ValueError('Index length mismatch: %d vs. %d' %
(len(index), N))
data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
return self._init_dict(data, index, columns, dtype)
def _init_matrix(self, data, index, columns, dtype=None):
""" Init self from ndarray or list of lists """
data = _prep_ndarray(data, copy=False)
index, columns = self._prep_index(data, index, columns)
data = {idx: data[:, i] for i, idx in enumerate(columns)}
return self._init_dict(data, index, columns, dtype)
def _init_matrix(self, data, index, columns, dtype=None):
""" Init self from ndarray or list of lists """
data = _prep_ndarray(data, copy=False)
index, columns = self._prep_index(data, index, columns)
data = {idx: data[:, i] for i, idx in enumerate(columns)}
return self._init_dict(data, index, columns, dtype)
