def _init_matrix(self, values, index, columns, dtype): if not isinstance(values, np.ndarray): arr = np.array(values) if issubclass(arr.dtype.type, basestring): arr = np.array(values, dtype=object, copy=True) values = arr if values.ndim == 1: N = values.shape[0] if N == 0: values = values.reshape((values.shape[0], 0)) else: values = values.reshape((values.shape[0], 1)) if dtype is not None: try: values = values.astype(dtype) except Exception: pass N, K = values.shape if index is None: index = _default_index(N) if columns is None: columns = _default_index(K) return index, columns, values
def _prep_index(self, data, index, columns): 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)) return index, columns
def _prep_index(self, data, index, columns): 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: {columns} vs. {K}' .format(columns=len(columns), K=K)) if len(index) != N: raise ValueError('Index length mismatch: {index} vs. {N}' .format(index=len(index), N=N)) return index, columns
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)