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Merged
merged 1 commit into from
Jun 21, 2014
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Merged

BUG: df.reset_index loses tz #7533

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2 changes: 1 addition & 1 deletion doc/source/v0.14.1.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -252,7 +252,7 @@ Bug Fixes

- Bug in ``Index.astype(float)`` where it would return an ``object`` dtype
``Index`` (:issue:`7464`).

- Bug in ``DataFrame.reset_index`` loses ``tz`` (:issue:`3950`)


- Bug in non-monotonic ``Index.union`` may preserve ``name`` incorrectly (:issue:`7458`)
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41 changes: 20 additions & 21 deletions pandas/core/frame.py
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Expand Up @@ -2326,19 +2326,24 @@ def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
else:
new_obj = self.copy()

def _maybe_cast(values, labels=None):

if values.dtype == np.object_:
values = lib.maybe_convert_objects(values)

# if we have the labels, extract the values with a mask
if labels is not None:
mask = labels == -1
values = values.take(labels)
if mask.any():
values, changed = com._maybe_upcast_putmask(
values, mask, np.nan)

def _maybe_casted_values(index, labels=None):
if isinstance(index, PeriodIndex):
values = index.asobject
elif (isinstance(index, DatetimeIndex) and
index.tz is not None):
values = index.asobject
else:
values = index.values
if values.dtype == np.object_:
values = lib.maybe_convert_objects(values)

# if we have the labels, extract the values with a mask
if labels is not None:
mask = labels == -1
values = values.take(labels)
if mask.any():
values, changed = com._maybe_upcast_putmask(values,
mask, np.nan)
return values

new_index = np.arange(len(new_obj))
Expand Down Expand Up @@ -2371,7 +2376,7 @@ def _maybe_cast(values, labels=None):
col_name = tuple(name_lst)

# to ndarray and maybe infer different dtype
level_values = _maybe_cast(lev.values, lab)
level_values = _maybe_casted_values(lev, lab)
if level is None or i in level:
new_obj.insert(0, col_name, level_values)

Expand All @@ -2387,13 +2392,7 @@ def _maybe_cast(values, labels=None):
lev_num = self.columns._get_level_number(col_level)
name_lst[lev_num] = name
name = tuple(name_lst)
if isinstance(self.index, PeriodIndex):
values = self.index.asobject
elif (isinstance(self.index, DatetimeIndex) and
self.index.tz is not None):
values = self.index.asobject
else:
values = _maybe_cast(self.index.values)
values = _maybe_casted_values(self.index)
new_obj.insert(0, name, values)

new_obj.index = new_index
Expand Down
14 changes: 14 additions & 0 deletions pandas/tests/test_frame.py
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Expand Up @@ -2182,6 +2182,20 @@ def test_set_index_cast_datetimeindex(self):
df.pop('ts')
assert_frame_equal(df, expected)

# GH 3950
# reset_index with single level
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
idx = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz, name='idx')
df = pd.DataFrame({'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = pd.DataFrame({'idx': [datetime(2011, 1, 1), datetime(2011, 1, 2),
datetime(2011, 1, 3), datetime(2011, 1, 4),
datetime(2011, 1, 5)],
'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx', 'a', 'b'])
expected['idx'] = expected['idx'].apply(lambda d: pd.Timestamp(d, tz=tz))
assert_frame_equal(df.reset_index(), expected)

def test_set_index_multiindexcolumns(self):
columns = MultiIndex.from_tuples([('foo', 1), ('foo', 2), ('bar', 1)])
df = DataFrame(np.random.randn(3, 3), columns=columns)
Expand Down
40 changes: 40 additions & 0 deletions pandas/tests/test_multilevel.py
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Expand Up @@ -2078,6 +2078,46 @@ def test_set_index_datetime(self):
self.assertTrue(df.index.get_level_values(1).equals(idx2))
self.assertTrue(df.index.get_level_values(2).equals(idx3))

def test_reset_index_datetime(self):
# GH 3950
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern']:
idx1 = pd.date_range('1/1/2011', periods=5, freq='D', tz=tz, name='idx1')
idx2 = pd.Index(range(5), name='idx2')
idx = pd.MultiIndex.from_arrays([idx1, idx2])
df = pd.DataFrame({'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = pd.DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': range(5),
'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(lambda d: pd.Timestamp(d, tz=tz))
assert_frame_equal(df.reset_index(), expected)

idx3 = pd.date_range('1/1/2012', periods=5, freq='MS', tz='Europe/Paris', name='idx3')
idx = pd.MultiIndex.from_arrays([idx1, idx2, idx3])
df = pd.DataFrame({'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']}, index=idx)

expected = pd.DataFrame({'idx1': [datetime.datetime(2011, 1, 1),
datetime.datetime(2011, 1, 2),
datetime.datetime(2011, 1, 3),
datetime.datetime(2011, 1, 4),
datetime.datetime(2011, 1, 5)],
'idx2': range(5),
'idx3': [datetime.datetime(2012, 1, 1),
datetime.datetime(2012, 2, 1),
datetime.datetime(2012, 3, 1),
datetime.datetime(2012, 4, 1),
datetime.datetime(2012, 5, 1)],
'a': range(5), 'b': ['A', 'B', 'C', 'D', 'E']},
columns=['idx1', 'idx2', 'idx3', 'a', 'b'])
expected['idx1'] = expected['idx1'].apply(lambda d: pd.Timestamp(d, tz=tz))
expected['idx3'] = expected['idx3'].apply(lambda d: pd.Timestamp(d, tz='Europe/Paris'))
assert_frame_equal(df.reset_index(), expected)

def test_set_index_period(self):
# GH 6631
df = DataFrame(np.random.random(6))
Expand Down