Python pandas.TimeGrouper() Examples
The following are 30
code examples of pandas.TimeGrouper().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
pandas
, or try the search function
.
.
Example #1
| Source File: test_resample.py From vnpy_crypto with MIT License | 7 votes |
|
def test_apply_iteration(self):
# #2300
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = DataFrame({'open': 1, 'close': 2}, index=ind)
tg = TimeGrouper('M')
_, grouper, _ = tg._get_grouper(df)
# Errors
grouped = df.groupby(grouper, group_keys=False)
f = lambda df: df['close'] / df['open']
# it works!
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
Example #2
| Source File: test_time_grouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_apply():
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
grouper = pd.TimeGrouper(freq='A', label='right', closed='right')
grouped = test_series.groupby(grouper)
def f(x):
return x.sort_values()[-3:]
applied = grouped.apply(f)
expected = test_series.groupby(lambda x: x.year).apply(f)
applied.index = applied.index.droplevel(0)
expected.index = expected.index.droplevel(0)
assert_series_equal(applied, expected)
Example #3
| Source File: test_timegrouper.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_timegrouper_apply_return_type_series(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_series(x):
return pd.Series([x['value'].sum()], ('sum',))
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)
result = (df_dt.groupby(pd.Grouper(freq='M', key='date'))
.apply(sumfunc_series))
assert_frame_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #4
| Source File: test_timegrouper.py From recruit with Apache License 2.0 | 6 votes |
|
def test_timegrouper_apply_return_type_value(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_value(x):
return x.value.sum()
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date'))
.apply(sumfunc_value))
assert_series_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #5
| Source File: test_time_grouper.py From recruit with Apache License 2.0 | 6 votes |
|
def test_panel_aggregation():
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
minor_axis=['A', 'B', 'C', 'D'])
tg = TimeGrouper('M', axis=1)
_, grouper, _ = tg._get_grouper(wp)
bingrouped = wp.groupby(grouper)
binagg = bingrouped.mean()
def f(x):
assert (isinstance(x, Panel))
return x.mean(1)
result = bingrouped.agg(f)
tm.assert_panel_equal(result, binagg)
Example #6
| Source File: test_time_grouper.py From recruit with Apache License 2.0 | 6 votes |
|
def test_aaa_group_order():
# GH 12840
# check TimeGrouper perform stable sorts
n = 20
data = np.random.randn(n, 4)
df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
datetime(2013, 1, 3), datetime(2013, 1, 4),
datetime(2013, 1, 5)] * 4
grouped = df.groupby(TimeGrouper(key='key', freq='D'))
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)),
df[::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)),
df[1::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)),
df[2::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)),
df[3::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)),
df[4::5])
Example #7
| Source File: test_time_grouper.py From recruit with Apache License 2.0 | 6 votes |
|
def test_aggregate_with_nat_size():
# GH 9925
n = 20
data = np.random.randn(n, 4).astype('int64')
normal_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
normal_df['key'] = [1, 2, np.nan, 4, 5] * 4
dt_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
dt_df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT,
datetime(2013, 1, 4), datetime(2013, 1, 5)] * 4
normal_grouped = normal_df.groupby('key')
dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
normal_result = normal_grouped.size()
dt_result = dt_grouped.size()
pad = Series([0], index=[3])
expected = normal_result.append(pad)
expected = expected.sort_index()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
assert_series_equal(expected, dt_result)
assert dt_result.index.name == 'key'
Example #8
| Source File: test_resample.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_apply_iteration(self):
# #2300
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = DataFrame({'open': 1, 'close': 2}, index=ind)
tg = TimeGrouper('M')
_, grouper, _ = tg._get_grouper(df)
# Errors
grouped = df.groupby(grouper, group_keys=False)
f = lambda df: df['close'] / df['open']
# it works!
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
Example #9
| Source File: test_resample.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_aaa_group_order(self):
# GH 12840
# check TimeGrouper perform stable sorts
n = 20
data = np.random.randn(n, 4)
df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
datetime(2013, 1, 3), datetime(2013, 1, 4),
datetime(2013, 1, 5)] * 4
grouped = df.groupby(TimeGrouper(key='key', freq='D'))
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)),
df[::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)),
df[1::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)),
df[2::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)),
df[3::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)),
df[4::5])
Example #10
| Source File: test_time_grouper.py From recruit with Apache License 2.0 | 6 votes |
|
def test_apply_iteration():
# #2300
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = DataFrame({'open': 1, 'close': 2}, index=ind)
tg = TimeGrouper('M')
_, grouper, _ = tg._get_grouper(df)
# Errors
grouped = df.groupby(grouper, group_keys=False)
def f(df):
return df['close'] / df['open']
# it works!
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
Example #11
| Source File: test_resample.py From vnpy_crypto with MIT License | 6 votes |
|
def test_aggregate_with_nat_size(self):
# GH 9925
n = 20
data = np.random.randn(n, 4).astype('int64')
normal_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
normal_df['key'] = [1, 2, np.nan, 4, 5] * 4
dt_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
dt_df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT,
datetime(2013, 1, 4), datetime(2013, 1, 5)] * 4
normal_grouped = normal_df.groupby('key')
dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
normal_result = normal_grouped.size()
dt_result = dt_grouped.size()
pad = Series([0], index=[3])
expected = normal_result.append(pad)
expected = expected.sort_index()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
assert_series_equal(expected, dt_result)
assert dt_result.index.name == 'key'
Example #12
| Source File: test_resample.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_resample_ohlc(self):
s = self.series
grouper = TimeGrouper(Minute(5))
expect = s.groupby(grouper).agg(lambda x: x[-1])
result = s.resample('5Min').ohlc()
assert len(result) == len(expect)
assert len(result.columns) == 4
xs = result.iloc[-2]
assert xs['open'] == s[-6]
assert xs['high'] == s[-6:-1].max()
assert xs['low'] == s[-6:-1].min()
assert xs['close'] == s[-2]
xs = result.iloc[0]
assert xs['open'] == s[0]
assert xs['high'] == s[:5].max()
assert xs['low'] == s[:5].min()
assert xs['close'] == s[4]
Example #13
| Source File: test_timegrouper.py From vnpy_crypto with MIT License | 6 votes |
|
def test_timegrouper_apply_return_type_series(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_series(x):
return pd.Series([x['value'].sum()], ('sum',))
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)
result = (df_dt.groupby(pd.Grouper(freq='M', key='date'))
.apply(sumfunc_series))
assert_frame_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #14
| Source File: test_timegrouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_timegrouper_apply_return_type_series(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_series(x):
return pd.Series([x['value'].sum()], ('sum',))
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)
result = (df_dt.groupby(pd.Grouper(freq='M', key='date'))
.apply(sumfunc_series))
assert_frame_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #15
| Source File: test_timegrouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_timegrouper_apply_return_type_value(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_value(x):
return x.value.sum()
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date'))
.apply(sumfunc_value))
assert_series_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #16
| Source File: test_resample.py From twitter-stock-recommendation with MIT License | 6 votes |
|
def test_resample_frame_basic(self):
df = tm.makeTimeDataFrame()
b = TimeGrouper('M')
g = df.groupby(b)
# check all cython functions work
funcs = ['add', 'mean', 'prod', 'min', 'max', 'var']
for f in funcs:
g._cython_agg_general(f)
result = df.resample('A').mean()
assert_series_equal(result['A'], df['A'].resample('A').mean())
result = df.resample('M').mean()
assert_series_equal(result['A'], df['A'].resample('M').mean())
df.resample('M', kind='period').mean()
df.resample('W-WED', kind='period').mean()
Example #17
| Source File: test_time_grouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_apply_iteration():
# #2300
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = DataFrame({'open': 1, 'close': 2}, index=ind)
tg = TimeGrouper('M')
_, grouper, _ = tg._get_grouper(df)
# Errors
grouped = df.groupby(grouper, group_keys=False)
def f(df):
return df['close'] / df['open']
# it works!
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
Example #18
| Source File: test_time_grouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_panel_aggregation():
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
minor_axis=['A', 'B', 'C', 'D'])
tg = TimeGrouper('M', axis=1)
_, grouper, _ = tg._get_grouper(wp)
bingrouped = wp.groupby(grouper)
binagg = bingrouped.mean()
def f(x):
assert (isinstance(x, Panel))
return x.mean(1)
result = bingrouped.agg(f)
tm.assert_panel_equal(result, binagg)
Example #19
| Source File: test_time_grouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_aaa_group_order():
# GH 12840
# check TimeGrouper perform stable sorts
n = 20
data = np.random.randn(n, 4)
df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
datetime(2013, 1, 3), datetime(2013, 1, 4),
datetime(2013, 1, 5)] * 4
grouped = df.groupby(TimeGrouper(key='key', freq='D'))
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)),
df[::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)),
df[1::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)),
df[2::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)),
df[3::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)),
df[4::5])
Example #20
| Source File: test_timegrouper.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_timegrouper_apply_return_type_value(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_value(x):
return x.value.sum()
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_value)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = (df_dt.groupby(pd.TimeGrouper(freq='M', key='date'))
.apply(sumfunc_value))
assert_series_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #21
| Source File: test_time_grouper.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_aggregate_with_nat_size():
# GH 9925
n = 20
data = np.random.randn(n, 4).astype('int64')
normal_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
normal_df['key'] = [1, 2, np.nan, 4, 5] * 4
dt_df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
dt_df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2), pd.NaT,
datetime(2013, 1, 4), datetime(2013, 1, 5)] * 4
normal_grouped = normal_df.groupby('key')
dt_grouped = dt_df.groupby(TimeGrouper(key='key', freq='D'))
normal_result = normal_grouped.size()
dt_result = dt_grouped.size()
pad = Series([0], index=[3])
expected = normal_result.append(pad)
expected = expected.sort_index()
expected.index = date_range(start='2013-01-01', freq='D',
periods=5, name='key')
assert_series_equal(expected, dt_result)
assert dt_result.index.name == 'key'
Example #22
| Source File: test_timegrouper.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_timegrouper_apply_return_type_series(self):
# Using `apply` with the `TimeGrouper` should give the
# same return type as an `apply` with a `Grouper`.
# Issue #11742
df = pd.DataFrame({'date': ['10/10/2000', '11/10/2000'],
'value': [10, 13]})
df_dt = df.copy()
df_dt['date'] = pd.to_datetime(df_dt['date'])
def sumfunc_series(x):
return pd.Series([x['value'].sum()], ('sum',))
expected = df.groupby(pd.Grouper(key='date')).apply(sumfunc_series)
result = (df_dt.groupby(pd.Grouper(freq='M', key='date'))
.apply(sumfunc_series))
assert_frame_equal(result.reset_index(drop=True),
expected.reset_index(drop=True))
Example #23
| Source File: ret.py From tia with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def compute(self, txns):
ltd = txns.pl.ltd_txn
grouper = pd.TimeGrouper(self.reset_freq)
period_rets = pd.Series(np.nan, index=ltd.index)
aum = self.aum
at = 0
cf = OrderedDict()
for key, grp in ltd.groupby(grouper):
if grp.empty:
continue
eod = aum + grp
sod = eod.shift(1)
sod.iloc[0] = aum
period_rets.iloc[at:at + len(grp.index)] = eod / sod - 1.
at += len(grp.index)
# get aum back to fixed amount
cf[key] = eod.iloc[-1] - aum
self.external_cash_flows = pd.Series(cf)
crets = CumulativeRets(period_rets)
return Performance(crets)
Example #24
| Source File: ret.py From tia with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def compute(self, txns):
ltd = txns.pl.ltd_txn
grouper = pd.TimeGrouper(self.freq)
period_rets = pd.Series(np.nan, index=ltd.index)
self.txn_aum = txn_aum = pd.Series(np.nan, index=ltd.index)
sop = self.starting_aum
at = 0
for key, grp in ltd.groupby(grouper):
if grp.empty:
continue
eod = sop + grp
sod = eod.shift(1)
sod.iloc[0] = sop
period_rets.iloc[at:at + len(grp.index)] = eod / sod - 1.
txn_aum.iloc[at:at + len(grp.index)] = sod
at += len(grp.index)
sop = eod.iloc[-1]
crets = CumulativeRets(period_rets)
return Performance(crets)
Example #25
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_resample_basic(self):
rng = date_range('1/1/2000 00:00:00', '1/1/2000 00:13:00', freq='min',
name='index')
s = Series(np.random.randn(14), index=rng)
result = s.resample('5min', closed='right', label='right').mean()
exp_idx = date_range('1/1/2000', periods=4, freq='5min', name='index')
expected = Series([s[0], s[1:6].mean(), s[6:11].mean(), s[11:].mean()],
index=exp_idx)
assert_series_equal(result, expected)
assert result.index.name == 'index'
result = s.resample('5min', closed='left', label='right').mean()
exp_idx = date_range('1/1/2000 00:05', periods=3, freq='5min',
name='index')
expected = Series([s[:5].mean(), s[5:10].mean(),
s[10:].mean()], index=exp_idx)
assert_series_equal(result, expected)
s = self.series
result = s.resample('5Min').last()
grouper = TimeGrouper(Minute(5), closed='left', label='left')
expect = s.groupby(grouper).agg(lambda x: x[-1])
assert_series_equal(result, expect)
Example #26
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_resample_frame_basic(self):
df = tm.makeTimeDataFrame()
b = TimeGrouper('M')
g = df.groupby(b)
# check all cython functions work
funcs = ['add', 'mean', 'prod', 'min', 'max', 'var']
for f in funcs:
g._cython_agg_general(f)
result = df.resample('A').mean()
assert_series_equal(result['A'], df['A'].resample('A').mean())
result = df.resample('M').mean()
assert_series_equal(result['A'], df['A'].resample('M').mean())
df.resample('M', kind='period').mean()
df.resample('W-WED', kind='period').mean()
Example #27
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_resample_ohlc(self):
s = self.series
grouper = TimeGrouper(Minute(5))
expect = s.groupby(grouper).agg(lambda x: x[-1])
result = s.resample('5Min').ohlc()
assert len(result) == len(expect)
assert len(result.columns) == 4
xs = result.iloc[-2]
assert xs['open'] == s[-6]
assert xs['high'] == s[-6:-1].max()
assert xs['low'] == s[-6:-1].min()
assert xs['close'] == s[-2]
xs = result.iloc[0]
assert xs['open'] == s[0]
assert xs['high'] == s[:5].max()
assert xs['low'] == s[:5].min()
assert xs['close'] == s[4]
Example #28
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_aaa_group_order(self):
# GH 12840
# check TimeGrouper perform stable sorts
n = 20
data = np.random.randn(n, 4)
df = DataFrame(data, columns=['A', 'B', 'C', 'D'])
df['key'] = [datetime(2013, 1, 1), datetime(2013, 1, 2),
datetime(2013, 1, 3), datetime(2013, 1, 4),
datetime(2013, 1, 5)] * 4
grouped = df.groupby(TimeGrouper(key='key', freq='D'))
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 1)),
df[::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 2)),
df[1::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 3)),
df[2::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 4)),
df[3::5])
tm.assert_frame_equal(grouped.get_group(datetime(2013, 1, 5)),
df[4::5])
Example #29
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_apply_iteration(self):
# #2300
N = 1000
ind = pd.date_range(start="2000-01-01", freq="D", periods=N)
df = DataFrame({'open': 1, 'close': 2}, index=ind)
tg = TimeGrouper('M')
_, grouper, _ = tg._get_grouper(df)
# Errors
grouped = df.groupby(grouper, group_keys=False)
f = lambda df: df['close'] / df['open']
# it works!
result = grouped.apply(f)
tm.assert_index_equal(result.index, df.index)
Example #30
| Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
def test_panel_aggregation(self):
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
with catch_warnings(record=True):
wp = Panel(data, items=['Item1', 'Item2'], major_axis=ind,
minor_axis=['A', 'B', 'C', 'D'])
tg = TimeGrouper('M', axis=1)
_, grouper, _ = tg._get_grouper(wp)
bingrouped = wp.groupby(grouper)
binagg = bingrouped.mean()
def f(x):
assert (isinstance(x, Panel))
return x.mean(1)
result = bingrouped.agg(f)
tm.assert_panel_equal(result, binagg)
