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)
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)
def Regroup(groupinfo, labels, *args):
"""
Modify the 3D numpy arrays in *args so that data is grouped
according to user specifications.
For example, presume that the following scenarios are given:
Fast_Down_01
Slow_Down_01
Fast_Down_02
Slow_Down_02
Fast_Down_04
Slow_Down_04
Fast_Down_08
Slow_Down_08
And only a single track run is specified: SCIT.
In this example, the third item, skill scores, can be arbitrary.
Now, supposed that we want to display the result data such that the x-axis
is for the Down* and there are two plots: one for Fast and one for Slow.
So, we group the scenarios data by some key (discussed later) _into_
the trackruns dimension. For this reason, the data dimension being
grouped into (in this case, trackruns) must originally be singleton.
*groupinfo* - dict with keys "group", "into", and "by".
The "group" element states which dimension the grouping will occur on.
The "into" element states along which dimension the groups will be
stacked. These two elements can have values of "scenarios", "skills",
or "trackruns".
The "by" element is rudimentary for now, but it controls the key value
function used for grouping. The key function is applied to the list of
default labels for the dimension stated for "group". The unique set of
keys generated by the function on these labels become the new default
labels for the "into" dimension.
Currently, the keyfunction is hard-coded to split the label by under-
scores and search for the string given in "by" in the resulting list.
It then returns the list's next value. So, in the above example, the
new labels for the "trackruns" dimension would be "01", "02", "04", and
"08".
"""
if groupinfo is None:
return args
if len(args) == 0:
return args
if len(labels[groupinfo['into']]) != 1:
raise ValueError("Dim %s is not singleton!" % groupinfo['into'])
if groupinfo['group'] == groupinfo['into']:
raise ValueError("Can not group %s dimension into itself!" %
groupinfo['group'])
from pandas import Panel
grpAxis = dataAxes[groupinfo['group']]
intoAxis = dataAxes[groupinfo['into']]
otherAxis = dataAxes[list(
set(['scenarios', 'trackruns', 'skills']) -
set([groupinfo['group'], groupinfo['into']]))[0]]
# !!Temporary!! restricted functionality for just trackrun variables
keyfunc = lambda x: _varval(x, groupinfo['by'])
g_args = []
for a in args:
wp = Panel(a,
items=labels['scenarios'],
major_axis=labels['skills'],
minor_axis=labels['trackruns'])
grouped = wp.groupby(keyfunc, axis=grpAxis)
if len(grouped) == 0:
raise ValueError("Grouping didn't result in anything!")
intolabs, g_a = zip(*grouped)
# Get a list of numpy arrays from the list of Panels
g_a = np.concatenate(map(lambda x: x.values, g_a), axis=intoAxis)
g_args.append(g_a)
labels[groupinfo['into']] = intolabs
# Do the full set for error-checking purposes
trunclabs = None
for intolab in intolabs:
# TODO: Generalize this!
# Take some original labels and remove the variable and its value that
# were used to make *intolabs*
tmp = [
'_'.join(_remove_varval(lab.split('_'), groupinfo['by']))
for lab in grouped.groups[intolab]
]
if trunclabs is not None:
if tmp != trunclabs:
raise ValueError("The labels do not match! %s\n%s" %
(trunclabs, tmp))
else:
trunclabs = tmp
labels[groupinfo['group']] = trunclabs
return g_args
def Regroup(groupinfo, labels, *args) :
"""
Modify the 3D numpy arrays in *args so that data is grouped
according to user specifications.
For example, presume that the following scenarios are given:
Fast_Down_01
Slow_Down_01
Fast_Down_02
Slow_Down_02
Fast_Down_04
Slow_Down_04
Fast_Down_08
Slow_Down_08
And only a single track run is specified: SCIT.
In this example, the third item, skill scores, can be arbitrary.
Now, supposed that we want to display the result data such that the x-axis
is for the Down* and there are two plots: one for Fast and one for Slow.
So, we group the scenarios data by some key (discussed later) _into_
the trackruns dimension. For this reason, the data dimension being
grouped into (in this case, trackruns) must originally be singleton.
*groupinfo* - dict with keys "group", "into", and "by".
The "group" element states which dimension the grouping will occur on.
The "into" element states along which dimension the groups will be
stacked. These two elements can have values of "scenarios", "skills",
or "trackruns".
The "by" element is rudimentary for now, but it controls the key value
function used for grouping. The key function is applied to the list of
default labels for the dimension stated for "group". The unique set of
keys generated by the function on these labels become the new default
labels for the "into" dimension.
Currently, the keyfunction is hard-coded to split the label by under-
scores and search for the string given in "by" in the resulting list.
It then returns the list's next value. So, in the above example, the
new labels for the "trackruns" dimension would be "01", "02", "04", and
"08".
"""
if groupinfo is None :
return args
if len(args) == 0 :
return args
if len(labels[groupinfo['into']]) != 1 :
raise ValueError("Dim %s is not singleton!" % groupinfo['into'])
if groupinfo['group'] == groupinfo['into'] :
raise ValueError("Can not group %s dimension into itself!" %
groupinfo['group'])
from pandas import Panel
grpAxis = dataAxes[groupinfo['group']]
intoAxis = dataAxes[groupinfo['into']]
otherAxis = dataAxes[list(set(['scenarios', 'trackruns', 'skills']) -
set([groupinfo['group'], groupinfo['into']]))[0]]
# !!Temporary!! restricted functionality for just trackrun variables
keyfunc = lambda x : _varval(x, groupinfo['by'])
g_args = []
for a in args :
wp = Panel(a, items=labels['scenarios'],
major_axis=labels['skills'],
minor_axis=labels['trackruns'])
grouped = wp.groupby(keyfunc, axis=grpAxis)
if len(grouped) == 0 :
raise ValueError("Grouping didn't result in anything!")
intolabs, g_a = zip(*grouped)
# Get a list of numpy arrays from the list of Panels
g_a = np.concatenate(map(lambda x : x.values, g_a),
axis=intoAxis)
g_args.append(g_a)
labels[groupinfo['into']] = intolabs
# Do the full set for error-checking purposes
trunclabs = None
for intolab in intolabs :
# TODO: Generalize this!
# Take some original labels and remove the variable and its value that
# were used to make *intolabs*
tmp = ['_'.join(_remove_varval(lab.split('_'), groupinfo['by'])) for
lab in grouped.groups[intolab]]
if trunclabs is not None :
if tmp != trunclabs :
raise ValueError("The labels do not match! %s\n%s" %
(trunclabs, tmp))
else :
trunclabs = tmp
labels[groupinfo['group']] = trunclabs
return g_args
