def downsample(self, freq, closed=None, label=None, axis=0, drop_empty=True):
"""
Essentially use resample logic but reutrning the groupby object
"""
# default closed/label on offset
defaults = _offset_defaults(freq)
if closed is None:
closed = defaults['closed']
if label is None:
label = defaults['label']
tg = TimeGrouper(freq, closed=closed, label=label, axis=axis)
grouper = tg.get_grouper(self)
# drop empty groups. this is when we have irregular data that
# we just want to group into Daily without creating empty days.
if drop_empty:
bins = [0] # start with 0 for np.diff
bins.extend(grouper.bins)
bins = np.array(bins)
periods_in_bin = np.diff(bins)
empty = periods_in_bin == 0
binlabels = grouper.binlabels
# skip the 0 we added
bins = bins[1:][~empty]
binlabels = binlabels[~empty]
grouper = BinGrouper(bins, binlabels)
return self.groupby(grouper, axis=axis)
def test_panel_aggregation(self):
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
wp = pd.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)
def test_panel_aggregation(self):
ind = pd.date_range("1/1/2000", periods=100)
data = np.random.randn(2, len(ind), 4)
wp = pd.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)
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)
self.assertTrue(result.index.equals(df.index))
def test_panelgroupby(self):
def agg_func(pan):
assert isinstance(pan, pd.Panel)
return pan.mean()
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2,len(ind),4)
wp = pd.Panel(data, items=['Item1', 'Item2'], major_axis=ind, minor_axis=['A', 'B', 'C', 'D'])
from pandas.tseries.resample import TimeGrouper
#timegrouper
tg = TimeGrouper('M', axis=1)
grouper = tg.get_grouper(wp)
bingrouped = wp.groupby(grouper)
# Failed 12-15-12
# https://github.com/pydata/pandas/issues/2537
bingrouped.agg(agg_func)
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)
self.assertTrue(result.index.equals(df.index))
def test_panel_aggregation(self):
ind = pd.date_range('1/1/2000', periods=100)
data = np.random.randn(2, len(ind), 4)
wp = pd.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)
def downsample(self, freq, closed=None, label=None, axis=0):
"""
Essentially use resample logic but reutrning the groupby object
"""
# default closed/label on offset
defaults = _offset_defaults(freq)
if closed is None:
closed = defaults['closed']
if label is None:
label = defaults['label']
tg = TimeGrouper(freq, closed=closed, label=label, axis=axis)
grouper = tg.get_grouper(self)
# TODO Get rid of empty bins?
#bins = [0]
#bins.extend(grouper.bins)
#periods_in_bin = np.diff(bins)
return self.groupby(grouper, axis=axis)
def downsample(self, freq, closed='right', label='right', axis=0):
tg = TimeGrouper(freq, closed=closed, label=label)
grouper = tg.get_grouper(self)
return self.groupby(grouper, axis=axis)
def downsample(self, freq, closed='right', label='right', axis=0):
tg = TimeGrouper(freq, closed=closed, label=label)
grouper = tg.get_grouper(self)
return self.groupby(grouper, axis=axis)
