/usr/share/pyshared/pandas/tests/test_groupby.py is in python-pandas 0.7.0-1.
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1422 1423 1424 1425 1426 1427 | import nose
import unittest
from datetime import datetime
from numpy import nan
from pandas.core.daterange import DateRange
from pandas.core.index import Index, MultiIndex
from pandas.core.common import rands
from pandas.core.frame import DataFrame
from pandas.core.groupby import GroupByError
from pandas.core.series import Series
from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
assert_series_equal, assert_almost_equal)
from pandas.core.panel import Panel
from pandas.tools.merge import concat
from collections import defaultdict
import pandas.core.datetools as dt
import numpy as np
import pandas.util.testing as tm
def commonSetUp(self):
self.dateRange = DateRange('1/1/2005', periods=250, offset=dt.bday)
self.stringIndex = Index([rands(8).upper() for x in xrange(250)])
self.groupId = Series([x[0] for x in self.stringIndex],
index=self.stringIndex)
self.groupDict = dict((k, v) for k, v in self.groupId.iteritems())
self.columnIndex = Index(['A', 'B', 'C', 'D', 'E'])
randMat = np.random.randn(250, 5)
self.stringMatrix = DataFrame(randMat, columns=self.columnIndex,
index=self.stringIndex)
self.timeMatrix = DataFrame(randMat, columns=self.columnIndex,
index=self.dateRange)
class TestGroupBy(unittest.TestCase):
def setUp(self):
self.ts = tm.makeTimeSeries()
self.seriesd = tm.getSeriesData()
self.tsd = tm.getTimeSeriesData()
self.frame = DataFrame(self.seriesd)
self.tsframe = DataFrame(self.tsd)
self.df = DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B' : ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'C' : np.random.randn(8),
'D' : np.random.randn(8)})
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.mframe = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self.three_group = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B' : ['one', 'one', 'one', 'two',
'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C' : ['dull', 'dull', 'shiny', 'dull',
'dull', 'shiny', 'shiny', 'dull',
'shiny', 'shiny', 'shiny'],
'D' : np.random.randn(11),
'E' : np.random.randn(11),
'F' : np.random.randn(11)})
def test_basic(self):
data = Series(np.arange(9) // 3, index=np.arange(9))
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
for k, v in grouped:
self.assertEqual(len(v), 3)
agged = grouped.aggregate(np.mean)
self.assertEqual(agged[1], 1)
assert_series_equal(agged, grouped.agg(np.mean)) # shorthand
assert_series_equal(agged, grouped.mean())
# Cython only returning floating point for now...
assert_series_equal(grouped.agg(np.sum).astype(float),
grouped.sum())
transformed = grouped.transform(lambda x: x * x.sum())
self.assertEqual(transformed[7], 12)
value_grouped = data.groupby(data)
assert_series_equal(value_grouped.aggregate(np.mean), agged)
# complex agg
agged = grouped.aggregate([np.mean, np.std])
agged = grouped.aggregate({'one' : np.mean,
'two' : np.std})
group_constants = {
0 : 10,
1 : 20,
2 : 30
}
agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
self.assertEqual(agged[1], 21)
# corner cases
self.assertRaises(Exception, grouped.aggregate, lambda x: x * 2)
def test_groupby_dict_mapping(self):
# GH #679
from pandas import Series
s = Series({'T1': 5})
result = s.groupby({'T1': 'T2'}).agg(sum)
expected = s.groupby(['T2']).agg(sum)
assert_series_equal(result, expected)
s = Series([1., 2., 3., 4.], index=list('abcd'))
mapping = {'a' : 0, 'b' : 0, 'c' : 1, 'd' : 1}
result = s.groupby(mapping).mean()
result2 = s.groupby(mapping).agg(np.mean)
expected = s.groupby([0, 0, 1, 1]).mean()
expected2 = s.groupby([0, 0, 1, 1]).mean()
assert_series_equal(result, expected)
assert_series_equal(result, result2)
assert_series_equal(result, expected2)
def test_groupby_nonobject_dtype(self):
key = self.mframe.index.labels[0]
grouped = self.mframe.groupby(key)
result = grouped.sum()
expected = self.mframe.groupby(key.astype('O')).sum()
assert_frame_equal(result, expected)
def test_agg_regression1(self):
grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
def test_agg_datetimes_mixed(self):
data = [[1, '2012-01-01', 1.0],
[2, '2012-01-02', 2.0],
[3, None, 3.0]]
df1 = DataFrame({'key': [x[0] for x in data],
'date': [x[1] for x in data],
'value': [x[2] for x in data]})
data = [[row[0], datetime.strptime(row[1], '%Y-%m-%d').date()
if row[1] else None, row[2]] for row in data]
df2 = DataFrame({'key': [x[0] for x in data],
'date': [x[1] for x in data],
'value': [x[2] for x in data]})
df1['weights'] = df1['value']/df1['value'].sum()
gb1 = df1.groupby('date').aggregate(np.sum)
df2['weights'] = df1['value']/df1['value'].sum()
gb2 = df2.groupby('date').aggregate(np.sum)
assert(len(gb1) == len(gb2))
def test_agg_must_agg(self):
grouped = self.df.groupby('A')['C']
self.assertRaises(Exception, grouped.agg, lambda x: x.describe())
self.assertRaises(Exception, grouped.agg, lambda x: x.index[:2])
def test_get_group(self):
wp = tm.makePanel()
grouped = wp.groupby(lambda x: x.month, axis='major')
gp = grouped.get_group(1)
expected = wp.reindex(major=[x for x in wp.major_axis if x.month == 1])
assert_panel_equal(gp, expected)
def test_agg_apply_corner(self):
# nothing to group, all NA
grouped = self.ts.groupby(self.ts * np.nan)
assert_series_equal(grouped.sum(), Series([]))
assert_series_equal(grouped.agg(np.sum), Series([]))
assert_series_equal(grouped.apply(np.sum), Series([]))
# DataFrame
grouped = self.tsframe.groupby(self.tsframe['A'] * np.nan)
assert_frame_equal(grouped.sum(),
DataFrame(columns=self.tsframe.columns))
assert_frame_equal(grouped.agg(np.sum), DataFrame({}))
assert_frame_equal(grouped.apply(np.sum), DataFrame({}))
def test_agg_python_multiindex(self):
grouped = self.mframe.groupby(['A', 'B'])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_apply_describe_bug(self):
grouped = self.mframe.groupby(level='first')
result = grouped.describe() # it works!
def test_len(self):
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year,
lambda x: x.month,
lambda x: x.day])
self.assertEquals(len(grouped), len(df))
grouped = df.groupby([lambda x: x.year,
lambda x: x.month])
expected = len(set([(x.year, x.month) for x in df.index]))
self.assertEquals(len(grouped), expected)
def test_groups(self):
grouped = self.df.groupby(['A'])
groups = grouped.groups
self.assert_(groups is grouped.groups) # caching works
for k, v in grouped.groups.iteritems():
self.assert_((self.df.ix[v]['A'] == k).all())
grouped = self.df.groupby(['A', 'B'])
groups = grouped.groups
self.assert_(groups is grouped.groups) # caching works
for k, v in grouped.groups.iteritems():
self.assert_((self.df.ix[v]['A'] == k[0]).all())
self.assert_((self.df.ix[v]['B'] == k[1]).all())
def test_aggregate_str_func(self):
def _check_results(grouped):
# single series
result = grouped['A'].agg('std')
expected = grouped['A'].std()
assert_series_equal(result, expected)
# group frame by function name
result = grouped.aggregate('var')
expected = grouped.var()
assert_frame_equal(result, expected)
# group frame by function dict
result = grouped.agg({'A' : 'var', 'B' : 'std', 'C' : 'mean'})
expected = DataFrame({'A' : grouped['A'].var(),
'B' : grouped['B'].std(),
'C' : grouped['C'].mean()})
assert_frame_equal(result, expected)
by_weekday = self.tsframe.groupby(lambda x: x.weekday())
_check_results(by_weekday)
by_mwkday = self.tsframe.groupby([lambda x: x.month,
lambda x: x.weekday()])
_check_results(by_mwkday)
def test_basic_regression(self):
# regression
T = [1.0*x for x in range(1,10) *10][:1095]
result = Series(T, range(0, len(T)))
groupings = np.random.random((1100,))
groupings = Series(groupings, range(0, len(groupings))) * 10.
grouped = result.groupby(groupings)
grouped.mean()
def test_transform(self):
data = Series(np.arange(9) // 3, index=np.arange(9))
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
transformed = grouped.transform(lambda x: x * x.sum())
self.assertEqual(transformed[7], 12)
def test_transform_broadcast(self):
grouped = self.ts.groupby(lambda x: x.month)
result = grouped.transform(np.mean)
self.assert_(result.index.equals(self.ts.index))
for _, gp in grouped:
self.assert_((result.reindex(gp.index) == gp.mean()).all())
grouped = self.tsframe.groupby(lambda x: x.month)
result = grouped.transform(np.mean)
self.assert_(result.index.equals(self.tsframe.index))
for _, gp in grouped:
agged = gp.mean()
res = result.reindex(gp.index)
for col in self.tsframe:
self.assert_((res[col] == agged[col]).all())
# group columns
grouped = self.tsframe.groupby({'A' : 0, 'B' : 0, 'C' : 1, 'D' : 1},
axis=1)
result = grouped.transform(np.mean)
self.assert_(result.index.equals(self.tsframe.index))
self.assert_(result.columns.equals(self.tsframe.columns))
for _, gp in grouped:
agged = gp.mean(1)
res = result.reindex(columns=gp.columns)
for idx in gp.index:
self.assert_((res.xs(idx) == agged[idx]).all())
def test_transform_multiple(self):
grouped = self.ts.groupby([lambda x: x.year, lambda x: x.month])
transformed = grouped.transform(lambda x: x * 2)
broadcasted = grouped.transform(np.mean)
def test_dispatch_transform(self):
df = self.tsframe[::5].reindex(self.tsframe.index)
grouped = df.groupby(lambda x: x.month)
filled = grouped.fillna(method='pad')
fillit = lambda x: x.fillna(method='pad')
expected = df.groupby(lambda x: x.month).transform(fillit)
assert_frame_equal(filled, expected)
def test_with_na(self):
index = Index(np.arange(10))
values = Series(np.ones(10), index)
labels = Series([nan, 'foo', 'bar', 'bar', nan, nan, 'bar',
'bar', nan, 'foo'], index=index)
grouped = values.groupby(labels)
agged = grouped.agg(len)
expected = Series([4, 2], index=['bar', 'foo'])
assert_series_equal(agged, expected, check_dtype=False)
self.assert_(issubclass(agged.dtype.type, np.integer))
def test_attr_wrapper(self):
grouped = self.ts.groupby(lambda x: x.weekday())
result = grouped.std()
expected = grouped.agg(lambda x: np.std(x, ddof=1))
assert_series_equal(result, expected)
# this is pretty cool
result = grouped.describe()
expected = {}
for name, gp in grouped:
expected[name] = gp.describe()
expected = DataFrame(expected).T
assert_frame_equal(result, expected)
# get attribute
result = grouped.dtype
expected = grouped.agg(lambda x: x.dtype)
# make sure raises error
self.assertRaises(AttributeError, getattr, grouped, 'foo')
def test_series_describe_multikey(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.describe()
assert_series_equal(result['mean'], grouped.mean())
assert_series_equal(result['std'], grouped.std())
assert_series_equal(result['min'], grouped.min())
def test_series_describe_single(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby(lambda x: x.month)
result = grouped.apply(lambda x: x.describe())
expected = grouped.describe()
assert_frame_equal(result, expected)
def test_series_agg_multikey(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.sum)
expected = grouped.sum()
assert_series_equal(result, expected)
def test_series_agg_multi_pure_python(self):
data = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B' : ['one', 'one', 'one', 'two',
'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C' : ['dull', 'dull', 'shiny', 'dull',
'dull', 'shiny', 'shiny', 'dull',
'shiny', 'shiny', 'shiny'],
'D' : np.random.randn(11),
'E' : np.random.randn(11),
'F' : np.random.randn(11)})
def bad(x):
assert(len(x.base) == len(x))
return 'foo'
result = data.groupby(['A', 'B']).agg(bad)
expected = data.groupby(['A', 'B']).agg(lambda x: 'foo')
assert_frame_equal(result, expected)
def test_series_index_name(self):
grouped = self.df.ix[:, ['C']].groupby(self.df['A'])
result = grouped.agg(lambda x: x.mean())
self.assertEqual(result.index.name, 'A')
def test_frame_describe_multikey(self):
grouped = self.tsframe.groupby([lambda x: x.year,
lambda x: x.month])
result = grouped.describe()
for col in self.tsframe:
expected = grouped[col].describe()
assert_frame_equal(result[col].unstack(), expected)
groupedT = self.tsframe.groupby({'A' : 0, 'B' : 0,
'C' : 1, 'D' : 1}, axis=1)
result = groupedT.describe()
for name, group in groupedT:
assert_frame_equal(result[name], group.describe())
def test_frame_groupby(self):
grouped = self.tsframe.groupby(lambda x: x.weekday())
# aggregate
aggregated = grouped.aggregate(np.mean)
self.assertEqual(len(aggregated), 5)
self.assertEqual(len(aggregated.columns), 4)
# by string
tscopy = self.tsframe.copy()
tscopy['weekday'] = [x.weekday() for x in tscopy.index]
stragged = tscopy.groupby('weekday').aggregate(np.mean)
assert_frame_equal(stragged, aggregated)
# transform
transformed = grouped.transform(lambda x: x - x.mean())
self.assertEqual(len(transformed), 30)
self.assertEqual(len(transformed.columns), 4)
# transform propagate
transformed = grouped.transform(lambda x: x.mean())
for name, group in grouped:
mean = group.mean()
for idx in group.index:
assert_almost_equal(transformed.xs(idx), mean)
# iterate
for weekday, group in grouped:
self.assert_(group.index[0].weekday() == weekday)
# groups / group_indices
groups = grouped.primary.groups
indices = grouped.primary.indices
for k, v in groups.iteritems():
samething = self.tsframe.index.take(indices[k])
self.assert_(np.array_equal(v, samething))
def test_frame_groupby_columns(self):
mapping = {
'A' : 0, 'B' : 0, 'C' : 1, 'D' : 1
}
grouped = self.tsframe.groupby(mapping, axis=1)
# aggregate
aggregated = grouped.aggregate(np.mean)
self.assertEqual(len(aggregated), len(self.tsframe))
self.assertEqual(len(aggregated.columns), 2)
# transform
tf = lambda x: x - x.mean()
groupedT = self.tsframe.T.groupby(mapping, axis=0)
assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))
# iterate
for k, v in grouped:
self.assertEqual(len(v.columns), 2)
def test_frame_set_name_single(self):
grouped = self.df.groupby('A')
result = grouped.mean()
self.assert_(result.index.name == 'A')
result = self.df.groupby('A', as_index=False).mean()
self.assert_(result.index.name != 'A')
result = grouped.agg(np.mean)
self.assert_(result.index.name == 'A')
result = grouped.agg({'C' : np.mean, 'D' : np.std})
self.assert_(result.index.name == 'A')
result = grouped['C'].mean()
self.assert_(result.index.name == 'A')
result = grouped['C'].agg(np.mean)
self.assert_(result.index.name == 'A')
result = grouped['C'].agg([np.mean, np.std])
self.assert_(result.index.name == 'A')
result = grouped['C'].agg({'foo' : np.mean, 'bar' : np.std})
self.assert_(result.index.name == 'A')
def test_multi_iter(self):
s = Series(np.arange(6))
k1 = np.array(['a', 'a', 'a', 'b', 'b', 'b'])
k2 = np.array(['1', '2', '1', '2', '1', '2'])
grouped = s.groupby([k1, k2])
iterated = list(grouped)
expected = [('a', '1', s[[0, 2]]),
('a', '2', s[[1]]),
('b', '1', s[[4]]),
('b', '2', s[[3, 5]])]
for i, ((one, two), three) in enumerate(iterated):
e1, e2, e3 = expected[i]
self.assert_(e1 == one)
self.assert_(e2 == two)
assert_series_equal(three, e3)
def test_multi_iter_frame(self):
k1 = np.array(['b', 'b', 'b', 'a', 'a', 'a'])
k2 = np.array(['1', '2', '1', '2', '1', '2'])
df = DataFrame({'v1' : np.random.randn(6),
'v2' : np.random.randn(6),
'k1' : k1, 'k2' : k2},
index=['one', 'two', 'three', 'four', 'five', 'six'])
grouped = df.groupby(['k1', 'k2'])
# things get sorted!
iterated = list(grouped)
idx = df.index
expected = [('a', '1', df.ix[idx[[4]]]),
('a', '2', df.ix[idx[[3, 5]]]),
('b', '1', df.ix[idx[[0, 2]]]),
('b', '2', df.ix[idx[[1]]])]
for i, ((one, two), three) in enumerate(iterated):
e1, e2, e3 = expected[i]
self.assert_(e1 == one)
self.assert_(e2 == two)
assert_frame_equal(three, e3)
# don't iterate through groups with no data
df['k1'] = np.array(['b', 'b', 'b', 'a', 'a', 'a'])
df['k2'] = np.array(['1', '1', '1', '2', '2', '2'])
grouped = df.groupby(['k1', 'k2'])
groups = {}
for key, gp in grouped:
groups[key] = gp
self.assertEquals(len(groups), 2)
# axis = 1
three_levels = self.three_group.groupby(['A', 'B', 'C']).mean()
grouped = three_levels.T.groupby(axis=1, level=(1, 2))
for key, group in grouped:
pass
def test_multi_iter_panel(self):
wp = tm.makePanel()
grouped = wp.groupby([lambda x: x.month, lambda x: x.weekday()],
axis=1)
for (month, wd), group in grouped:
exp_axis = [x for x in wp.major_axis
if x.month == month and x.weekday() == wd]
expected = wp.reindex(major=exp_axis)
assert_panel_equal(group, expected)
def test_multi_func(self):
col1 = self.df['A']
col2 = self.df['B']
grouped = self.df.groupby([col1.get, col2.get])
agged = grouped.mean()
expected = self.df.groupby(['A', 'B']).mean()
assert_frame_equal(agged.ix[:, ['C', 'D']],
expected.ix[:, ['C', 'D']])
# some "groups" with no data
df = DataFrame({'v1' : np.random.randn(6),
'v2' : np.random.randn(6),
'k1' : np.array(['b', 'b', 'b', 'a', 'a', 'a']),
'k2' : np.array(['1', '1', '1', '2', '2', '2'])},
index=['one', 'two', 'three', 'four', 'five', 'six'])
# only verify that it works for now
grouped = df.groupby(['k1', 'k2'])
grouped.agg(np.sum)
def test_multi_key_multiple_functions(self):
grouped = self.df.groupby(['A', 'B'])['C']
agged = grouped.agg([np.mean, np.std])
expected = DataFrame({'mean' : grouped.agg(np.mean),
'std' : grouped.agg(np.std)})
assert_frame_equal(agged, expected)
def test_frame_multi_key_function_list(self):
data = DataFrame({'A' : ['foo', 'foo', 'foo', 'foo',
'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B' : ['one', 'one', 'one', 'two',
'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C' : ['dull', 'dull', 'shiny', 'dull',
'dull', 'shiny', 'shiny', 'dull',
'shiny', 'shiny', 'shiny'],
'D' : np.random.randn(11),
'E' : np.random.randn(11),
'F' : np.random.randn(11)})
grouped = data.groupby(['A', 'B'])
funcs = [np.mean, np.std]
agged = grouped.agg(funcs)
expected = concat([grouped['D'].agg(funcs), grouped['E'].agg(funcs),
grouped['F'].agg(funcs)],
keys=['D', 'E', 'F'], axis=1)
assert(isinstance(agged.index, MultiIndex))
assert(isinstance(expected.index, MultiIndex))
assert_frame_equal(agged, expected)
def test_groupby_multiple_columns(self):
data = self.df
grouped = data.groupby(['A', 'B'])
def _check_op(op):
result1 = op(grouped)
expected = defaultdict(dict)
for n1, gp1 in data.groupby('A'):
for n2, gp2 in gp1.groupby('B'):
expected[n1][n2] = op(gp2.ix[:, ['C', 'D']])
expected = dict((k, DataFrame(v)) for k, v in expected.iteritems())
expected = Panel.fromDict(expected).swapaxes(0, 1)
# a little bit crude
for col in ['C', 'D']:
result_col = op(grouped[col])
exp = expected[col]
pivoted = result1[col].unstack()
pivoted2 = result_col.unstack()
assert_frame_equal(pivoted.reindex_like(exp), exp)
assert_frame_equal(pivoted2.reindex_like(exp), exp)
_check_op(lambda x: x.sum())
_check_op(lambda x: x.mean())
# test single series works the same
result = data['C'].groupby([data['A'], data['B']]).mean()
expected = data.groupby(['A', 'B']).mean()['C']
assert_series_equal(result, expected)
def test_groupby_as_index_agg(self):
grouped = self.df.groupby('A', as_index=False)
# single-key
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
result2 = grouped.agg({'C' : np.mean, 'D' : np.sum})
expected2 = grouped.mean()
expected2['D'] = grouped.sum()['D']
assert_frame_equal(result2, expected2)
# multi-key
grouped = self.df.groupby(['A', 'B'], as_index=False)
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
result2 = grouped.agg({'C' : np.mean, 'D' : np.sum})
expected2 = grouped.mean()
expected2['D'] = grouped.sum()['D']
assert_frame_equal(result2, expected2)
def test_as_index_series_return_frame(self):
grouped = self.df.groupby('A', as_index=False)
grouped2 = self.df.groupby(['A', 'B'], as_index=False)
result = grouped['C'].agg(np.sum)
expected = grouped.agg(np.sum).ix[:, ['A', 'C']]
self.assert_(isinstance(result, DataFrame))
assert_frame_equal(result, expected)
result2 = grouped2['C'].agg(np.sum)
expected2 = grouped2.agg(np.sum).ix[:, ['A', 'B', 'C']]
self.assert_(isinstance(result2, DataFrame))
assert_frame_equal(result2, expected2)
result = grouped['C'].sum()
expected = grouped.sum().ix[:, ['A', 'C']]
self.assert_(isinstance(result, DataFrame))
assert_frame_equal(result, expected)
result2 = grouped2['C'].sum()
expected2 = grouped2.sum().ix[:, ['A', 'B', 'C']]
self.assert_(isinstance(result2, DataFrame))
assert_frame_equal(result2, expected2)
# corner case
self.assertRaises(Exception, grouped['C'].__getitem__,
'D')
def test_groupby_as_index_cython(self):
data = self.df
# single-key
grouped = data.groupby('A', as_index=False)
result = grouped.mean()
expected = data.groupby(['A']).mean()
expected.insert(0, 'A', expected.index)
expected.index = np.arange(len(expected))
assert_frame_equal(result, expected)
# multi-key
grouped = data.groupby(['A', 'B'], as_index=False)
result = grouped.mean()
expected = data.groupby(['A', 'B']).mean()
arrays = zip(*expected.index.get_tuple_index())
expected.insert(0, 'A', arrays[0])
expected.insert(1, 'B', arrays[1])
expected.index = np.arange(len(expected))
assert_frame_equal(result, expected)
def test_groupby_as_index_series_scalar(self):
grouped = self.df.groupby(['A', 'B'], as_index=False)
# GH #421
result = grouped['C'].agg(len)
expected = grouped.agg(len).ix[:, ['A', 'B', 'C']]
assert_frame_equal(result, expected)
def test_groupby_as_index_corner(self):
self.assertRaises(TypeError, self.ts.groupby,
lambda x: x.weekday(), as_index=False)
self.assertRaises(ValueError, self.df.groupby,
lambda x: x.lower(), as_index=False, axis=1)
def test_groupby_multiple_key(self):
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year,
lambda x: x.month,
lambda x: x.day])
agged = grouped.sum()
assert_almost_equal(df.values, agged.values)
grouped = df.T.groupby([lambda x: x.year,
lambda x: x.month,
lambda x: x.day], axis=1)
agged = grouped.agg(lambda x: x.sum(1))
self.assert_(agged.index.equals(df.columns))
assert_almost_equal(df.T.values, agged.values)
agged = grouped.agg(lambda x: x.sum(1))
assert_almost_equal(df.T.values, agged.values)
def test_groupby_multi_corner(self):
# test that having an all-NA column doesn't mess you up
df = self.df.copy()
df['bad'] = np.nan
agged = df.groupby(['A', 'B']).mean()
expected = self.df.groupby(['A', 'B']).mean()
expected['bad'] = np.nan
assert_frame_equal(agged, expected)
def test_omit_nuisance(self):
grouped = self.df.groupby('A')
result = grouped.mean()
expected = self.df.ix[:, ['A', 'C', 'D']].groupby('A').mean()
assert_frame_equal(result, expected)
agged = grouped.agg(np.mean)
exp = grouped.mean()
assert_frame_equal(agged, exp)
df = self.df.ix[:, ['A', 'C', 'D']]
df['E'] = datetime.now()
grouped = df.groupby('A')
result = grouped.agg(np.sum)
expected = grouped.sum()
assert_frame_equal(result, expected)
# won't work with axis = 1
grouped = df.groupby({'A' : 0, 'C' : 0, 'D' : 1, 'E' : 1}, axis=1)
result = self.assertRaises(TypeError, grouped.agg, np.sum)
def test_omit_nuisance_python_multiple(self):
grouped = self.three_group.groupby(['A', 'B'])
agged = grouped.agg(np.mean)
exp = grouped.mean()
assert_frame_equal(agged, exp)
def test_empty_groups_corner(self):
# handle empty groups
df = DataFrame({'k1' : np.array(['b', 'b', 'b', 'a', 'a', 'a']),
'k2' : np.array(['1', '1', '1', '2', '2', '2']),
'k3' : ['foo', 'bar'] * 3,
'v1' : np.random.randn(6),
'v2' : np.random.randn(6)})
grouped = df.groupby(['k1', 'k2'])
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
grouped = self.mframe[3:5].groupby(level=0)
agged = grouped.apply(lambda x: x.mean())
agged_A = grouped['A'].apply(np.mean)
assert_series_equal(agged['A'], agged_A)
self.assertEquals(agged.index.name, 'first')
def test_apply_concat_preserve_names(self):
grouped = self.three_group.groupby(['A', 'B'])
def desc(group):
result = group.describe()
result.index.name = 'stat'
return result
def desc2(group):
result = group.describe()
result.index.name = 'stat'
result = result[:len(group)]
# weirdo
return result
def desc3(group):
result = group.describe()
# names are different
result.index.name = 'stat_%d' % len(group)
result = result[:len(group)]
# weirdo
return result
result = grouped.apply(desc)
self.assertEquals(result.index.names, ['A', 'B', 'stat'])
result2 = grouped.apply(desc2)
self.assertEquals(result2.index.names, ['A', 'B', 'stat'])
result3 = grouped.apply(desc3)
self.assertEquals(result3.index.names, ['A', 'B', None])
def test_nonsense_func(self):
df = DataFrame([0])
self.assertRaises(Exception, df.groupby, lambda x: x + 'foo')
def test_cythonized_aggers(self):
data = {'A' : [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan],
'B' : ['A', 'B'] * 6,
'C' : np.random.randn(12)}
df = DataFrame(data)
df['C'][2:10:2] = nan
def _testit(op):
# single column
grouped = df.drop(['B'], axis=1).groupby('A')
exp = {}
for cat, group in grouped:
exp[cat] = op(group['C'])
exp = DataFrame({'C' : exp})
result = op(grouped)
assert_frame_equal(result, exp)
# multiple columns
grouped = df.groupby(['A', 'B'])
expd = {}
for (cat1, cat2), group in grouped:
expd.setdefault(cat1, {})[cat2] = op(group['C'])
exp = DataFrame(expd).T.stack(dropna=False)
result = op(grouped)['C']
assert_series_equal(result, exp)
_testit(lambda x: x.sum())
_testit(lambda x: x.mean())
def test_cython_agg_boolean(self):
frame = DataFrame({'a': np.random.randint(0, 5, 50),
'b': np.random.randint(0, 2, 50).astype('bool')})
result = frame.groupby('a')['b'].mean()
expected = frame.groupby('a')['b'].agg(np.mean)
assert_series_equal(result, expected)
def test_cython_agg_nothing_to_agg(self):
frame = DataFrame({'a': np.random.randint(0, 5, 50),
'b': ['foo', 'bar'] * 25})
self.assertRaises(GroupByError, frame.groupby('a')['b'].mean)
frame = DataFrame({'a': np.random.randint(0, 5, 50),
'b': ['foo', 'bar'] * 25})
self.assertRaises(GroupByError, frame[['b']].groupby(frame['a']).mean)
def test_wrap_aggregated_output_multindex(self):
df = self.mframe.T
df['baz', 'two'] = 'peekaboo'
keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
agged = df.groupby(keys).agg(np.mean)
self.assert_(isinstance(agged.columns, MultiIndex))
def test_grouping_attrs(self):
deleveled = self.mframe.reset_index()
grouped = deleveled.groupby(['first', 'second'])
for i, ping in enumerate(grouped.groupings):
the_counts = self.mframe.groupby(level=i).count()['A']
other_counts = Series(ping.counts, ping.group_index)
assert_almost_equal(the_counts,
other_counts.reindex(the_counts.index))
# compute counts when group by level
grouped = self.mframe.groupby(level=0)
ping = grouped.groupings[0]
the_counts = grouped.size()
other_counts = Series(ping.counts, ping.group_index)
assert_almost_equal(the_counts,
other_counts.reindex(the_counts.index))
def test_groupby_level(self):
frame = self.mframe
deleveled = frame.reset_index()
result0 = frame.groupby(level=0).sum()
result1 = frame.groupby(level=1).sum()
expected0 = frame.groupby(deleveled['first'].values).sum()
expected1 = frame.groupby(deleveled['second'].values).sum()
expected0 = expected0.reindex(frame.index.levels[0])
expected1 = expected1.reindex(frame.index.levels[1])
self.assert_(result0.index.name == 'first')
self.assert_(result1.index.name == 'second')
assert_frame_equal(result0, expected0)
assert_frame_equal(result1, expected1)
self.assertEquals(result0.index.name, frame.index.names[0])
self.assertEquals(result1.index.name, frame.index.names[1])
# groupby level name
result0 = frame.groupby(level='first').sum()
result1 = frame.groupby(level='second').sum()
assert_frame_equal(result0, expected0)
assert_frame_equal(result1, expected1)
# axis=1
result0 = frame.T.groupby(level=0, axis=1).sum()
result1 = frame.T.groupby(level=1, axis=1).sum()
assert_frame_equal(result0, expected0.T)
assert_frame_equal(result1, expected1.T)
# raise exception for non-MultiIndex
self.assertRaises(ValueError, self.df.groupby, level=0)
def test_groupby_level_apply(self):
frame = self.mframe
result = frame.groupby(level=0).count()
self.assert_(result.index.name == 'first')
result = frame.groupby(level=1).count()
self.assert_(result.index.name == 'second')
result = frame['A'].groupby(level=0).count()
self.assert_(result.index.name == 'first')
def test_groupby_level_mapper(self):
frame = self.mframe
deleveled = frame.reset_index()
mapper0 = {'foo' : 0, 'bar' : 0,
'baz' : 1, 'qux' : 1}
mapper1 = {'one' : 0, 'two' : 0, 'three' : 1}
result0 = frame.groupby(mapper0, level=0).sum()
result1 = frame.groupby(mapper1, level=1).sum()
mapped_level0 = np.array([mapper0.get(x) for x in deleveled['first']])
mapped_level1 = np.array([mapper1.get(x) for x in deleveled['second']])
expected0 = frame.groupby(mapped_level0).sum()
expected1 = frame.groupby(mapped_level1).sum()
assert_frame_equal(result0, expected0)
assert_frame_equal(result1, expected1)
def test_level_preserve_order(self):
grouped = self.mframe.groupby(level=0)
exp_labels = np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3])
assert_almost_equal(grouped.groupings[0].labels, exp_labels)
def test_grouping_labels(self):
grouped = self.mframe.groupby(self.mframe.index.get_level_values(0))
exp_labels = np.array([2, 2, 2, 0, 0, 1, 1, 3, 3, 3])
assert_almost_equal(grouped.groupings[0].labels, exp_labels)
def test_cython_fail_agg(self):
dr = DateRange('1/1/2000', periods=50)
ts = Series(['A', 'B', 'C', 'D', 'E'] * 10, index=dr)
grouped = ts.groupby(lambda x: x.month)
summed = grouped.sum()
expected = grouped.agg(np.sum)
assert_series_equal(summed, expected)
def test_apply_series_to_frame(self):
def f(piece):
return DataFrame({'value' : piece,
'demeaned' : piece - piece.mean(),
'logged' : np.log(piece)})
dr = DateRange('1/1/2000', periods=100)
ts = Series(np.random.randn(100), index=dr)
grouped = ts.groupby(lambda x: x.month)
result = grouped.apply(f)
self.assert_(isinstance(result, DataFrame))
self.assert_(result.index.equals(ts.index))
def test_apply_frame_to_series(self):
grouped = self.df.groupby(['A', 'B'])
result = grouped.apply(len)
expected = grouped.count()['C']
self.assert_(result.index.equals(expected.index))
self.assert_(np.array_equal(result.values, expected.values))
def test_apply_transform(self):
grouped = self.ts.groupby(lambda x: x.month)
result = grouped.apply(lambda x: x * 2)
expected = grouped.transform(lambda x: x * 2)
assert_series_equal(result, expected)
def test_apply_multikey_corner(self):
grouped = self.tsframe.groupby([lambda x: x.year,
lambda x: x.month])
def f(group):
return group.sort('A')[-5:]
result = grouped.apply(f)
for key, group in grouped:
assert_frame_equal(result.ix[key], f(group))
def test_groupby_series_indexed_differently(self):
s1 = Series([5.0,-9.0,4.0,100.,-5.,55.,6.7],
index=Index(['a','b','c','d','e','f','g']))
s2 = Series([1.0,1.0,4.0,5.0,5.0,7.0],
index=Index(['a','b','d','f','g','h']))
grouped = s1.groupby(s2)
agged = grouped.mean()
exp = s1.groupby(s2.reindex(s1.index).get).mean()
assert_series_equal(agged, exp)
def test_groupby_with_hier_columns(self):
tuples = zip(*[['bar', 'bar', 'baz', 'baz',
'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two',
'one', 'two', 'one', 'two']])
index = MultiIndex.from_tuples(tuples)
columns = MultiIndex.from_tuples([('A', 'cat'), ('B', 'dog'),
('B', 'cat'), ('A', 'dog')])
df = DataFrame(np.random.randn(8, 4), index=index,
columns=columns)
result = df.groupby(level=0).mean()
self.assert_(result.columns.equals(columns))
result = df.groupby(level=0, axis=1).mean()
self.assert_(result.index.equals(df.index))
result = df.groupby(level=0).agg(np.mean)
self.assert_(result.columns.equals(columns))
result = df.groupby(level=0).apply(lambda x: x.mean())
self.assert_(result.columns.equals(columns))
result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
self.assert_(result.columns.equals(Index(['A', 'B'])))
self.assert_(result.index.equals(df.index))
# add a nuisance column
sorted_columns, _ = columns.sortlevel(0)
df['A', 'foo'] = 'bar'
result = df.groupby(level=0).mean()
self.assert_(result.columns.equals(sorted_columns))
def test_pass_args_kwargs(self):
from scipy.stats import scoreatpercentile
def f(x, q=None):
return scoreatpercentile(x, q)
g = lambda x: scoreatpercentile(x, 80)
# Series
ts_grouped = self.ts.groupby(lambda x: x.month)
agg_result = ts_grouped.agg(scoreatpercentile, 80)
apply_result = ts_grouped.apply(scoreatpercentile, 80)
trans_result = ts_grouped.transform(scoreatpercentile, 80)
agg_expected = ts_grouped.quantile(.8)
trans_expected = ts_grouped.transform(g)
assert_series_equal(apply_result, agg_expected)
assert_series_equal(agg_result, agg_expected)
assert_series_equal(trans_result, trans_expected)
agg_result = ts_grouped.agg(f, q=80)
apply_result = ts_grouped.apply(f, q=80)
trans_result = ts_grouped.transform(f, q=80)
assert_series_equal(agg_result, agg_expected)
assert_series_equal(apply_result, agg_expected)
assert_series_equal(trans_result, trans_expected)
# DataFrame
df_grouped = self.tsframe.groupby(lambda x: x.month)
agg_result = df_grouped.agg(scoreatpercentile, 80)
apply_result = df_grouped.apply(DataFrame.quantile, .8)
expected = df_grouped.quantile(.8)
assert_frame_equal(apply_result, expected)
assert_frame_equal(agg_result, expected)
agg_result = df_grouped.agg(f, q=80)
apply_result = df_grouped.apply(DataFrame.quantile, q=.8)
assert_frame_equal(agg_result, expected)
assert_frame_equal(apply_result, expected)
# def test_cython_na_bug(self):
# values = np.random.randn(10)
# shape = (5, 5)
# label_list = [np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2], dtype=np.int32),
# np.array([1, 2, 3, 4, 0, 1, 2, 3, 3, 4], dtype=np.int32)]
# lib.group_aggregate(values, label_list, shape)
def test_size(self):
grouped = self.df.groupby(['A', 'B'])
result = grouped.size()
for key, group in grouped:
self.assertEquals(result[key], len(group))
grouped = self.df.groupby('A')
result = grouped.size()
for key, group in grouped:
self.assertEquals(result[key], len(group))
grouped = self.df.groupby('B')
result = grouped.size()
for key, group in grouped:
self.assertEquals(result[key], len(group))
def test_grouping_ndarray(self):
grouped = self.df.groupby(self.df['A'].values)
result = grouped.sum()
expected = self.df.groupby('A').sum()
assert_frame_equal(result, expected)
def test_apply_typecast_fail(self):
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)})
def f(group):
v = group['v']
group['v2'] = (v - v.min()) / (v.max() - v.min())
return group
result = df.groupby('d').apply(f)
expected = df.copy()
expected['v2'] = np.tile([0., 0.5, 1], 2)
assert_frame_equal(result, expected)
def test_apply_multiindex_fail(self):
index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
[1, 2, 3, 1, 2, 3]])
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)}, index=index)
def f(group):
v = group['v']
group['v2'] = (v - v.min()) / (v.max() - v.min())
return group
result = df.groupby('d').apply(f)
expected = df.copy()
expected['v2'] = np.tile([0., 0.5, 1], 2)
assert_frame_equal(result, expected)
def test_apply_corner(self):
result = self.tsframe.groupby(lambda x: x.year).apply(lambda x: x * 2)
expected = self.tsframe * 2
assert_frame_equal(result, expected)
def test_transform_mixed_type(self):
index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
[1, 2, 3, 1, 2, 3]])
df = DataFrame({'d' : [1.,1.,1.,2.,2.,2.],
'c' : np.tile(['a','b','c'], 2),
'v' : np.arange(1., 7.)}, index=index)
def f(group):
group['g'] = group['d'] * 2
return group[:1]
grouped = df.groupby('c')
result = grouped.apply(f)
self.assert_(result['d'].dtype == np.float64)
for key, group in grouped:
res = f(group)
assert_frame_equal(res, result.ix[key])
def test_groupby_wrong_multi_labels(self):
from pandas import read_csv
from cStringIO import StringIO
data = """index,foo,bar,baz,spam,data
0,foo1,bar1,baz1,spam2,20
1,foo1,bar2,baz1,spam3,30
2,foo2,bar2,baz1,spam2,40
3,foo1,bar1,baz2,spam1,50
4,foo3,bar1,baz2,spam1,60"""
data = read_csv(StringIO(data), index_col=0)
grouped = data.groupby(['foo', 'bar', 'baz', 'spam'])
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
def test_groupby_series_with_name(self):
result = self.df.groupby(self.df['A']).mean()
result2 = self.df.groupby(self.df['A'], as_index=False).mean()
self.assertEquals(result.index.name, 'A')
self.assert_('A' in result2)
result = self.df.groupby([self.df['A'], self.df['B']]).mean()
result2 = self.df.groupby([self.df['A'], self.df['B']],
as_index=False).mean()
self.assertEquals(result.index.names, ['A', 'B'])
self.assert_('A' in result2)
self.assert_('B' in result2)
def test_groupby_nonstring_columns(self):
df = DataFrame([np.arange(10) for x in range(10)])
grouped = df.groupby(0)
result = grouped.mean()
expected = df.groupby(df[0]).mean()
del expected[0]
assert_frame_equal(result, expected)
def test_cython_grouper_series_bug_noncontig(self):
arr = np.empty((100, 100))
arr.fill(np.nan)
obj = Series(arr[:, 0], index=range(100))
inds = np.tile(range(10), 10)
result = obj.groupby(inds).agg(Series.median)
self.assert_(result.isnull().all())
def test_convert_objects_leave_decimal_alone(self):
from decimal import Decimal
s = Series(range(5))
labels = np.array(['a', 'b', 'c', 'd', 'e'], dtype='O')
def convert_fast(x):
return Decimal(str(x.mean()))
def convert_force_pure(x):
# base will be length 0
assert(len(x.base) == len(x))
return Decimal(str(x.mean()))
grouped = s.groupby(labels)
result = grouped.agg(convert_fast)
self.assert_(result.dtype == np.object_)
self.assert_(isinstance(result[0], Decimal))
result = grouped.agg(convert_force_pure)
self.assert_(result.dtype == np.object_)
self.assert_(isinstance(result[0], Decimal))
def test_groupby_list_infer_array_like(self):
result = self.df.groupby(list(self.df['A'])).mean()
expected = self.df.groupby(self.df['A']).mean()
assert_frame_equal(result, expected)
self.assertRaises(Exception, self.df.groupby, list(self.df['A'][:-1]))
# pathological case of ambiguity
df = DataFrame({'foo' : [0, 1], 'bar' : [3, 4],
'val' : np.random.randn(2)})
result = df.groupby(['foo', 'bar']).mean()
expected = df.groupby([df['foo'], df['bar']]).mean()[['val']]
def test_dictify(self):
dict(iter(self.df.groupby('A')))
dict(iter(self.df.groupby(['A', 'B'])))
dict(iter(self.df['C'].groupby(self.df['A'])))
dict(iter(self.df['C'].groupby([self.df['A'], self.df['B']])))
dict(iter(self.df.groupby('A')['C']))
dict(iter(self.df.groupby(['A', 'B'])['C']))
def test_sparse_friendly(self):
sdf = self.df[['C', 'D']].to_sparse()
panel = tm.makePanel()
tm.add_nans(panel)
def _check_work(gp):
gp.mean()
gp.agg(np.mean)
dict(iter(gp))
# it works!
_check_work(sdf.groupby(lambda x: x // 2))
_check_work(sdf['C'].groupby(lambda x: x // 2))
_check_work(sdf.groupby(self.df['A']))
# do this someday
# _check_work(panel.groupby(lambda x: x.month, axis=1))
def test_panel_groupby(self):
self.panel = tm.makePanel()
tm.add_nans(self.panel)
grouped = self.panel.groupby({'ItemA' : 0, 'ItemB' : 0, 'ItemC' : 1},
axis='items')
agged = grouped.agg(np.mean)
self.assert_(np.array_equal(agged.items, [0, 1]))
grouped = self.panel.groupby(lambda x: x.month, axis='major')
agged = grouped.agg(np.mean)
self.assert_(np.array_equal(agged.major_axis, [1, 2]))
grouped = self.panel.groupby({'A' : 0, 'B' : 0, 'C' : 1, 'D' : 1},
axis='minor')
agged = grouped.agg(np.mean)
self.assert_(np.array_equal(agged.minor_axis, [0, 1]))
def test_numpy_groupby(self):
from pandas.core.groupby import numpy_groupby
data = np.random.randn(100, 100)
labels = np.random.randint(0, 10, size=100)
df = DataFrame(data)
result = df.groupby(labels).sum().values
expected = numpy_groupby(data, labels)
assert_almost_equal(result, expected)
result = df.groupby(labels, axis=1).sum().values
expected = numpy_groupby(data, labels, axis=1)
assert_almost_equal(result, expected)
def test_decons():
from pandas.core.groupby import decons_group_index, get_group_index
def testit(label_list, shape):
group_index = get_group_index(label_list, shape)
label_list2 = decons_group_index(group_index, shape)
for a, b in zip(label_list, label_list2):
assert(np.array_equal(a, b))
shape = (4, 5, 6)
label_list = [np.tile([0, 1, 2, 3, 0, 1, 2, 3], 100),
np.tile([0, 2, 4, 3, 0, 1, 2, 3], 100),
np.tile([5, 1, 0, 2, 3, 0, 5, 4], 100)]
testit(label_list, shape)
shape = (10000, 10000)
label_list = [np.tile(np.arange(10000), 5),
np.tile(np.arange(10000), 5)]
testit(label_list, shape)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__,'-vvs','-x','--pdb', '--pdb-failure'],
exit=False)
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