/usr/lib/python2.7/dist-packages/pandas/tests/test_algos.py is in python-pandas 0.13.1-2ubuntu2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | from pandas.compat import range
import numpy as np
from pandas.core.api import Series, Categorical
import pandas as pd
import pandas.core.algorithms as algos
import pandas.util.testing as tm
class TestMatch(tm.TestCase):
_multiprocess_can_split_ = True
def test_ints(self):
values = np.array([0, 2, 1])
to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([0, 2, 1, 1, 0, 2, np.nan, 0]))
tm.assert_series_equal(result,expected)
s = pd.Series(np.arange(5),dtype=np.float32)
result = algos.match(s, [2,4])
expected = np.array([-1, -1, 0, -1, 1])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(s, [2,4], np.nan))
expected = Series(np.array([np.nan, np.nan, 0, np.nan, 1]))
tm.assert_series_equal(result,expected)
def test_strings(self):
values = ['foo', 'bar', 'baz']
to_match = ['bar', 'foo', 'qux', 'foo', 'bar', 'baz', 'qux']
result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([1, 0, np.nan, 0, 1, 2, np.nan]))
tm.assert_series_equal(result,expected)
class TestFactorize(tm.TestCase):
_multiprocess_can_split_ = True
def test_basic(self):
labels, uniques = algos.factorize(['a', 'b', 'b', 'a',
'a', 'c', 'c', 'c'])
self.assert_(np.array_equal(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array(['a','b','c'], dtype=object)))
labels, uniques = algos.factorize(['a', 'b', 'b', 'a',
'a', 'c', 'c', 'c'], sort=True)
self.assert_(np.array_equal(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array(['a','b','c'], dtype=object)))
labels, uniques = algos.factorize(list(reversed(range(5))))
self.assert_(np.array_equal(labels, np.array([0, 1, 2, 3, 4], dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([ 4, 3, 2, 1, 0],dtype=np.int64)))
labels, uniques = algos.factorize(list(reversed(range(5))), sort=True)
self.assert_(np.array_equal(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([0, 1, 2, 3, 4], dtype=np.int64)))
labels, uniques = algos.factorize(list(reversed(np.arange(5.))))
self.assert_(np.array_equal(labels, np.array([0., 1., 2., 3., 4.], dtype=np.float64)))
self.assert_(np.array_equal(uniques, np.array([ 4, 3, 2, 1, 0],dtype=np.int64)))
labels, uniques = algos.factorize(list(reversed(np.arange(5.))), sort=True)
self.assert_(np.array_equal(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([0., 1., 2., 3., 4.], dtype=np.float64)))
def test_mixed(self):
# doc example reshaping.rst
x = Series(['A', 'A', np.nan, 'B', 3.14, np.inf])
labels, uniques = algos.factorize(x)
self.assert_(np.array_equal(labels, np.array([ 0, 0, -1, 1, 2, 3],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array(['A', 'B', 3.14, np.inf], dtype=object)))
labels, uniques = algos.factorize(x, sort=True)
self.assert_(np.array_equal(labels, np.array([ 2, 2, -1, 3, 0, 1],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([3.14, np.inf, 'A', 'B'], dtype=object)))
def test_datelike(self):
# M8
v1 = pd.Timestamp('20130101 09:00:00.00004')
v2 = pd.Timestamp('20130101')
x = Series([v1,v1,v1,v2,v2,v1])
labels, uniques = algos.factorize(x)
self.assert_(np.array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([v1.value,v2.value],dtype='M8[ns]')))
labels, uniques = algos.factorize(x, sort=True)
self.assert_(np.array_equal(labels, np.array([ 1,1,1,0,0,1],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([v2.value,v1.value],dtype='M8[ns]')))
# period
v1 = pd.Period('201302',freq='M')
v2 = pd.Period('201303',freq='M')
x = Series([v1,v1,v1,v2,v2,v1])
# periods are not 'sorted' as they are converted back into an index
labels, uniques = algos.factorize(x)
self.assert_(np.array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([v1, v2],dtype=object)))
labels, uniques = algos.factorize(x,sort=True)
self.assert_(np.array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64)))
self.assert_(np.array_equal(uniques, np.array([v1, v2],dtype=object)))
class TestUnique(tm.TestCase):
_multiprocess_can_split_ = True
def test_ints(self):
arr = np.random.randint(0, 100, size=50)
result = algos.unique(arr)
tm.assert_isinstance(result, np.ndarray)
def test_objects(self):
arr = np.random.randint(0, 100, size=50).astype('O')
result = algos.unique(arr)
tm.assert_isinstance(result, np.ndarray)
def test_object_refcount_bug(self):
lst = ['A', 'B', 'C', 'D', 'E']
for i in range(1000):
len(algos.unique(lst))
def test_on_index_object(self):
mindex = pd.MultiIndex.from_arrays([np.arange(5).repeat(5),
np.tile(np.arange(5), 5)])
mindex = mindex.repeat(2)
result = pd.unique(mindex)
result.sort()
expected = mindex.values
expected.sort()
tm.assert_almost_equal(result, expected)
class TestValueCounts(tm.TestCase):
_multiprocess_can_split_ = True
def test_value_counts(self):
from pandas.tools.tile import cut
arr = np.random.randn(4)
factor = cut(arr, 4)
tm.assert_isinstance(factor, Categorical)
result = algos.value_counts(factor)
expected = algos.value_counts(np.asarray(factor))
tm.assert_series_equal(result, expected)
def test_value_counts_bins(self):
s = [1, 2, 3, 4]
result = algos.value_counts(s, bins=1)
self.assertEqual(result.tolist(), [4])
self.assertEqual(result.index[0], 0.997)
result = algos.value_counts(s, bins=2, sort=False)
self.assertEqual(result.tolist(), [2, 2])
self.assertEqual(result.index[0], 0.997)
self.assertEqual(result.index[1], 2.5)
def test_value_counts_dtypes(self):
result = algos.value_counts([1, 1.])
self.assertEqual(len(result), 1)
result = algos.value_counts([1, 1.], bins=1)
self.assertEqual(len(result), 1)
result = algos.value_counts(Series([1, 1., '1'])) # object
self.assertEqual(len(result), 2)
self.assertRaises(TypeError, lambda s: algos.value_counts(s, bins=1), ['1', 1])
def test_quantile():
s = Series(np.random.randn(100))
result = algos.quantile(s, [0, .25, .5, .75, 1.])
expected = algos.quantile(s.values, [0, .25, .5, .75, 1.])
tm.assert_almost_equal(result, expected)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
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