/usr/share/pyshared/pandas/tools/tests/test_pivot.py is in python-pandas 0.7.0-1.
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import numpy as np
from pandas import DataFrame, Series
from pandas.tools.merge import concat
from pandas.tools.pivot import pivot_table, crosstab
import pandas.util.testing as tm
class TestPivotTable(unittest.TestCase):
def setUp(self):
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 test_pivot_table(self):
rows = ['A', 'B']
cols= 'C'
table = pivot_table(self.data, values='D', rows=rows, cols=cols)
table2 = self.data.pivot_table(values='D', rows=rows, cols=cols)
tm.assert_frame_equal(table, table2)
# this works
pivot_table(self.data, values='D', rows=rows)
if len(rows) > 1:
self.assertEqual(table.index.names, rows)
else:
self.assertEqual(table.index.name, rows[0])
if len(cols) > 1:
self.assertEqual(table.columns.names, cols)
else:
self.assertEqual(table.columns.name, cols[0])
expected = self.data.groupby(rows + [cols])['D'].agg(np.mean).unstack()
tm.assert_frame_equal(table, expected)
def test_pivot_table_multiple(self):
rows = ['A', 'B']
cols= 'C'
table = pivot_table(self.data, rows=rows, cols=cols)
expected = self.data.groupby(rows + [cols]).agg(np.mean).unstack()
tm.assert_frame_equal(table, expected)
def test_pivot_multi_values(self):
result = pivot_table(self.data, values=['D', 'E'],
rows='A', cols=['B', 'C'], fill_value=0)
expected = pivot_table(self.data.drop(['F'], axis=1),
rows='A', cols=['B', 'C'], fill_value=0)
tm.assert_frame_equal(result, expected)
def test_pivot_multi_functions(self):
f = lambda func: pivot_table(self.data, values=['D', 'E'],
rows=['A', 'B'], cols='C',
aggfunc=func)
result = f([np.mean, np.std])
means = f(np.mean)
stds = f(np.std)
expected = concat([means, stds], keys=['mean', 'std'], axis=1)
tm.assert_frame_equal(result, expected)
# margins not supported??
f = lambda func: pivot_table(self.data, values=['D', 'E'],
rows=['A', 'B'], cols='C',
aggfunc=func, margins=True)
result = f([np.mean, np.std])
means = f(np.mean)
stds = f(np.std)
expected = concat([means, stds], keys=['mean', 'std'], axis=1)
tm.assert_frame_equal(result, expected)
def test_margins(self):
def _check_output(res, col, rows=['A', 'B'], cols=['C']):
cmarg = res['All'][:-1]
exp = self.data.groupby(rows)[col].mean()
tm.assert_series_equal(cmarg, exp)
rmarg = res.xs(('All', ''))[:-1]
exp = self.data.groupby(cols)[col].mean()
tm.assert_series_equal(rmarg, exp)
gmarg = res['All']['All', '']
exp = self.data[col].mean()
self.assertEqual(gmarg, exp)
# column specified
table = self.data.pivot_table('D', rows=['A', 'B'], cols='C',
margins=True, aggfunc=np.mean)
_check_output(table, 'D')
# no column specified
table = self.data.pivot_table(rows=['A', 'B'], cols='C',
margins=True, aggfunc=np.mean)
for valcol in table.columns.levels[0]:
_check_output(table[valcol], valcol)
# no col
# to help with a buglet
self.data.columns = [k * 2 for k in self.data.columns]
table = self.data.pivot_table(rows=['AA', 'BB'], margins=True,
aggfunc=np.mean)
for valcol in table.columns:
gmarg = table[valcol]['All', '']
self.assertEqual(gmarg, self.data[valcol].mean())
# this is OK
table = self.data.pivot_table(rows=['AA', 'BB'], margins=True,
aggfunc='mean')
# no rows
rtable = self.data.pivot_table(cols=['AA', 'BB'], margins=True,
aggfunc=np.mean)
self.assert_(isinstance(rtable, Series))
for item in ['DD', 'EE', 'FF']:
gmarg = table[item]['All', '']
self.assertEqual(gmarg, self.data[item].mean())
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
from pandas.util.compat import product
import datetime
import pandas
d = datetime.date.min
data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
[d + datetime.timedelta(i) for i in xrange(20)], [1.0]))
df = pandas.DataFrame(data)
table = df.pivot_table(values=4, rows=[0,1,3],cols=[2])
df2 = df.rename(columns=str)
table2 = df2.pivot_table(values='4', rows=['0','1','3'], cols=['2'])
tm.assert_frame_equal(table, table2)
class TestCrosstab(unittest.TestCase):
def setUp(self):
df = 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)})
self.df = df.append(df, ignore_index=True)
def test_crosstab_single(self):
df = self.df
result = crosstab(df['A'], df['C'])
expected = df.groupby(['A', 'C']).size().unstack()
tm.assert_frame_equal(result, expected.fillna(0).astype(np.int64))
def test_crosstab_multiple(self):
df = self.df
result = crosstab(df['A'], [df['B'], df['C']])
expected = df.groupby(['A', 'B', 'C']).size()
expected = expected.unstack('B').unstack('C').fillna(0).astype(np.int64)
tm.assert_frame_equal(result, expected)
result = crosstab([df['B'], df['C']], df['A'])
expected = df.groupby(['B', 'C', 'A']).size()
expected = expected.unstack('A').fillna(0).astype(np.int64)
tm.assert_frame_equal(result, expected)
def test_crosstab_ndarray(self):
a = np.random.randint(0, 5, size=100)
b = np.random.randint(0, 3, size=100)
c = np.random.randint(0, 10, size=100)
df = DataFrame({'a': a, 'b': b, 'c': c})
result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'))
expected = crosstab(df['a'], [df['b'], df['c']])
tm.assert_frame_equal(result, expected)
result = crosstab([b, c], a, colnames=['a'], rownames=('b', 'c'))
expected = crosstab([df['b'], df['c']], df['a'])
tm.assert_frame_equal(result, expected)
# assign arbitrary names
result = crosstab(self.df['A'].values, self.df['C'].values)
self.assertEqual(result.index.name, 'row_0')
self.assertEqual(result.columns.name, 'col_0')
def test_crosstab_margins(self):
a = np.random.randint(0, 7, size=100)
b = np.random.randint(0, 3, size=100)
c = np.random.randint(0, 5, size=100)
df = DataFrame({'a': a, 'b': b, 'c': c})
result = crosstab(a, [b, c], rownames=['a'], colnames=('b', 'c'),
margins=True)
self.assertEqual(result.index.names, ['a'])
self.assertEqual(result.columns.names, ['b', 'c'])
all_cols = result['All', '']
exp_cols = df.groupby(['a']).size().astype('i8')
exp_cols = exp_cols.append(Series([len(df)], index=['All']))
tm.assert_series_equal(all_cols, exp_cols)
all_rows = result.ix['All']
exp_rows = df.groupby(['b', 'c']).size().astype('i8')
exp_rows = exp_rows.append(Series([len(df)], index=[('All', '')]))
exp_rows = exp_rows.reindex(all_rows.index)
exp_rows = exp_rows.fillna(0).astype(np.int64)
tm.assert_series_equal(all_rows, exp_rows)
def test_crosstab_pass_values(self):
a = np.random.randint(0, 7, size=100)
b = np.random.randint(0, 3, size=100)
c = np.random.randint(0, 5, size=100)
values = np.random.randn(100)
table = crosstab([a, b], c, values, aggfunc=np.sum,
rownames=['foo', 'bar'], colnames=['baz'])
df = DataFrame({'foo': a, 'bar': b, 'baz': c, 'values' : values})
expected = df.pivot_table('values', rows=['foo', 'bar'], cols='baz',
aggfunc=np.sum)
tm.assert_frame_equal(table, expected)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__,'-vvs','-x','--ipdb', '--ipdb-failure'],
exit=False)
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