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