def _unconvert_index(data, kind):
if kind == 'datetime64':
index = DatetimeIndex(data)
elif kind == 'datetime':
index = np.array([datetime.fromtimestamp(v) for v in data],
dtype=object)
elif kind == 'date':
index = np.array([date.fromtimestamp(v) for v in data], dtype=object)
elif kind in ('string', 'integer', 'float'):
index = np.array(data)
elif kind == 'object':
index = np.array(data[0])
else: # pragma: no cover
raise ValueError('unrecognized index type %s' % kind)
return index
def test_groupby_groups_datetimeindex(self):
# #1430
from pandas.tseries.api import DatetimeIndex
periods = 1000
ind = DatetimeIndex(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({
'high': np.arange(periods),
'low': np.arange(periods)
},
index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
# it works!
groups = grouped.groups
tm.assertIsInstance(list(groups.keys())[0], datetime)
# GH 11442
index = pd.date_range('2015/01/01', periods=5, name='date')
df = pd.DataFrame({
'A': [5, 6, 7, 8, 9],
'B': [1, 2, 3, 4, 5]
},
index=index)
result = df.groupby(level='date').groups
dates = [
'2015-01-05', '2015-01-04', '2015-01-03', '2015-01-02',
'2015-01-01'
]
expected = {
pd.Timestamp(date): pd.DatetimeIndex([date], name='date')
for date in dates
}
tm.assert_dict_equal(result, expected)
grouped = df.groupby(level='date')
for date in dates:
result = grouped.get_group(date)
data = [[df.loc[date, 'A'], df.loc[date, 'B']]]
expected_index = pd.DatetimeIndex([date], name='date')
expected = pd.DataFrame(data,
columns=list('AB'),
index=expected_index)
tm.assert_frame_equal(result, expected)
def f(values, freq=None, tz=None):
return DatetimeIndex._simple_new(values, None, freq=freq, tz=tz)
def f(values, freq=None, tz=None):
return DatetimeIndex._simple_new(values, None, freq=freq,
tz=tz)