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TST: add tests for algos.factorize (GH6212) #6231

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Feb 2, 2014
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TST: add tests for algos.factorize (GH6212) #6231

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4 changes: 4 additions & 0 deletions pandas/core/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,6 +129,10 @@ def factorize(values, sort=False, order=None, na_sentinel=-1):

Returns
-------
labels : the indexer to the original array
uniques : the unique values

note: an array of Periods will ignore sort as it returns an always sorted PeriodIndex
"""
from pandas.tseries.period import PeriodIndex
vals = np.asarray(values)
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72 changes: 72 additions & 0 deletions pandas/tests/test_algos.py
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Expand Up @@ -44,6 +44,78 @@ def test_strings(self):
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

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