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)