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Feb 16, 2014
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CLN: Specialize assert_(np.array_equal(...)) #6368

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1 change: 1 addition & 0 deletions doc/source/release.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,7 @@ Improvements to existing features
- improve performance of slice indexing on Series with string keys (:issue:`6341`)
- implement joining a single-level indexed DataFrame on a matching column of a multi-indexed DataFrame (:issue:`3662`)
- Performance improvement in indexing into a multi-indexed Series (:issue:`5567`)
- Testing statements updated to use specialized asserts (:issue: `6175`)

.. _release.bug_fixes-0.14.0:

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54 changes: 27 additions & 27 deletions pandas/tests/test_algos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ def test_ints(self):

result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0])
self.assert_numpy_array_equals(result, expected)
self.assert_numpy_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]))
Expand All @@ -26,7 +26,7 @@ def test_ints(self):
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_numpy_array_equals(result, expected)
self.assert_numpy_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]))
Expand All @@ -38,7 +38,7 @@ def test_strings(self):

result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1])
self.assert_numpy_array_equals(result, expected)
self.assert_numpy_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]))
Expand All @@ -51,42 +51,42 @@ def test_basic(self):

labels, uniques = algos.factorize(['a', 'b', 'b', 'a',
'a', 'c', 'c', 'c'])
# self.assert_numpy_array_equals(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array(['a','b','c'], dtype=object))
# self.assert_numpy_array_equal(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64))
self.assert_numpy_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_numpy_array_equals(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array(['a','b','c'], dtype=object))
self.assert_numpy_array_equal(labels, np.array([ 0, 1, 1, 0, 0, 2, 2, 2],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array(['a','b','c'], dtype=object))

labels, uniques = algos.factorize(list(reversed(range(5))))
self.assert_numpy_array_equals(labels, np.array([0, 1, 2, 3, 4], dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_array_equal(labels, np.array([0, 1, 2, 3, 4], dtype=np.int64))
self.assert_numpy_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_numpy_array_equals(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([0, 1, 2, 3, 4], dtype=np.int64))
self.assert_numpy_array_equal(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([0, 1, 2, 3, 4], dtype=np.int64))

labels, uniques = algos.factorize(list(reversed(np.arange(5.))))
self.assert_numpy_array_equals(labels, np.array([0., 1., 2., 3., 4.], dtype=np.float64))
self.assert_numpy_array_equals(uniques, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_array_equal(labels, np.array([0., 1., 2., 3., 4.], dtype=np.float64))
self.assert_numpy_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_numpy_array_equals(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([0., 1., 2., 3., 4.], dtype=np.float64))
self.assert_numpy_array_equal(labels, np.array([ 4, 3, 2, 1, 0],dtype=np.int64))
self.assert_numpy_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_numpy_array_equals(labels, np.array([ 0, 0, -1, 1, 2, 3],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array(['A', 'B', 3.14, np.inf], dtype=object))
self.assert_numpy_array_equal(labels, np.array([ 0, 0, -1, 1, 2, 3],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array(['A', 'B', 3.14, np.inf], dtype=object))

labels, uniques = algos.factorize(x, sort=True)
self.assert_numpy_array_equals(labels, np.array([ 2, 2, -1, 3, 0, 1],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([3.14, np.inf, 'A', 'B'], dtype=object))
self.assert_numpy_array_equal(labels, np.array([ 2, 2, -1, 3, 0, 1],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([3.14, np.inf, 'A', 'B'], dtype=object))

def test_datelike(self):

Expand All @@ -95,12 +95,12 @@ def test_datelike(self):
v2 = pd.Timestamp('20130101')
x = Series([v1,v1,v1,v2,v2,v1])
labels, uniques = algos.factorize(x)
self.assert_numpy_array_equals(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([v1.value,v2.value],dtype='M8[ns]'))
self.assert_numpy_array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([v1.value,v2.value],dtype='M8[ns]'))

labels, uniques = algos.factorize(x, sort=True)
self.assert_numpy_array_equals(labels, np.array([ 1,1,1,0,0,1],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([v2.value,v1.value],dtype='M8[ns]'))
self.assert_numpy_array_equal(labels, np.array([ 1,1,1,0,0,1],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([v2.value,v1.value],dtype='M8[ns]'))

# period
v1 = pd.Period('201302',freq='M')
Expand All @@ -109,12 +109,12 @@ def test_datelike(self):

# periods are not 'sorted' as they are converted back into an index
labels, uniques = algos.factorize(x)
self.assert_numpy_array_equals(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([v1, v2],dtype=object))
self.assert_numpy_array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([v1, v2],dtype=object))

labels, uniques = algos.factorize(x,sort=True)
self.assert_numpy_array_equals(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equals(uniques, np.array([v1, v2],dtype=object))
self.assert_numpy_array_equal(labels, np.array([ 0,0,0,1,1,0],dtype=np.int64))
self.assert_numpy_array_equal(uniques, np.array([v1, v2],dtype=object))

class TestUnique(tm.TestCase):
_multiprocess_can_split_ = True
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6 changes: 3 additions & 3 deletions pandas/tests/test_base.py
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Expand Up @@ -102,7 +102,7 @@ def test_shallow_copying(self):
assert_isinstance(self.container.view(), FrozenNDArray)
self.assert_(not isinstance(self.container.view(np.ndarray), FrozenNDArray))
self.assert_(self.container.view() is not self.container)
self.assert_(np.array_equal(self.container, original))
self.assert_numpy_array_equal(self.container, original)
# shallow copy should be the same too
assert_isinstance(self.container._shallow_copy(), FrozenNDArray)
# setting should not be allowed
Expand All @@ -114,10 +114,10 @@ def test_values(self):
original = self.container.view(np.ndarray).copy()
n = original[0] + 15
vals = self.container.values()
self.assert_(np.array_equal(original, vals))
self.assert_numpy_array_equal(original, vals)
self.assert_(original is not vals)
vals[0] = n
self.assert_(np.array_equal(self.container, original))
self.assert_numpy_array_equal(self.container, original)
self.assertEqual(vals[0], n)


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6 changes: 3 additions & 3 deletions pandas/tests/test_categorical.py
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Expand Up @@ -82,11 +82,11 @@ def test_comparisons(self):
other = self.factor[np.random.permutation(n)]
result = self.factor == other
expected = np.asarray(self.factor) == np.asarray(other)
self.assert_(np.array_equal(result, expected))
self.assert_numpy_array_equal(result, expected)

result = self.factor == 'd'
expected = np.repeat(False, len(self.factor))
self.assert_(np.array_equal(result, expected))
self.assert_numpy_array_equal(result, expected)

def test_na_flags_int_levels(self):
# #1457
Expand All @@ -98,7 +98,7 @@ def test_na_flags_int_levels(self):
cat = Categorical(labels, levels)
repr(cat)

self.assert_(np.array_equal(com.isnull(cat), labels == -1))
self.assert_numpy_array_equal(com.isnull(cat), labels == -1)

def test_levels_none(self):
factor = Categorical(['a', 'b', 'b', 'a',
Expand Down
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