def _index_to_records(self, df): metadata = {} index = df.index if isinstance(index, MultiIndex): # array of tuples to numpy cols. copy copy copy if len(df) > 0: ix_vals = list(map(np.array, [index.get_level_values(i) for i in range(index.nlevels)])) else: # empty multi index has no size, create empty arrays for recarry.. ix_vals = [np.array([]) for n in index.names] else: ix_vals = [index.values] count = 0 index_names = list(index.names) if isinstance(index, MultiIndex): for i, n in enumerate(index_names): if n is None: index_names[i] = 'level_%d' % count count += 1 log.info("Level in MultiIndex has no name, defaulting to %s" % index_names[i]) elif index_names[0] is None: index_names = ['index'] log.info("Index has no name, defaulting to 'index'") metadata['index'] = index_names if isinstance(index, DatetimeIndex) and index.tz is not None: metadata['index_tz'] = get_timezone(index.tz) elif isinstance(index, MultiIndex): metadata['index_tz'] = [get_timezone(i.tz) if isinstance(i, DatetimeIndex) else None for i in index.levels] return index_names, ix_vals, metadata
def _infer(a, b): tz = a.tzinfo if b and b.tzinfo: if not (tslib.get_timezone(tz) == tslib.get_timezone(b.tzinfo)): raise AssertionError('Inputs must both have the same timezone,' ' {0} != {1}'.format(tz, b.tzinfo)) return tz
def _infer(a, b): tz = a.tzinfo if b and b.tzinfo: if not (tslib.get_timezone(tz) == tslib.get_timezone(b.tzinfo)): raise AssertionError('Inputs must both have the same timezone,' ' {0} != {1}'.format(tz, b.tzinfo)) return tz