def test_multiindex_objects(self):
mi = MultiIndex(levels=[['b', 'd', 'a'], [1, 2, 3]],
labels=[[0, 1, 0, 2], [2, 0, 0, 1]],
names=['col1', 'col2'])
recons = mi._sort_levels_monotonic()
# these are equal
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
# _hashed_values and hash_pandas_object(..., index=False)
# equivalency
expected = hash_pandas_object(
mi, index=False).values
result = mi._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = hash_pandas_object(
recons, index=False).values
result = recons._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = mi._hashed_values
result = recons._hashed_values
# values should match, but in different order
tm.assert_numpy_array_equal(np.sort(result),
np.sort(expected))
def test_multiindex_objects(self):
mi = MultiIndex(levels=[['b', 'd', 'a'], [1, 2, 3]],
labels=[[0, 1, 0, 2], [2, 0, 0, 1]],
names=['col1', 'col2'])
recons = mi._sort_levels_monotonic()
# these are equal
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
# _hashed_values and hash_pandas_object(..., index=False)
# equivalency
expected = hash_pandas_object(mi, index=False).values
result = mi._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = hash_pandas_object(recons, index=False).values
result = recons._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = mi._hashed_values
result = recons._hashed_values
# values should match, but in different order
tm.assert_numpy_array_equal(np.sort(result), np.sort(expected))
def check_not_equal_with_index(self, obj):
# check that we are not hashing the same if
# we include the index
if not isinstance(obj, Index):
a = hash_pandas_object(obj, index=True)
b = hash_pandas_object(obj, index=False)
self.assertFalse((a == b).all())
def check_not_equal_with_index(self, obj):
# check that we are not hashing the same if
# we include the index
if not isinstance(obj, Index):
a = hash_pandas_object(obj, index=True)
b = hash_pandas_object(obj, index=False)
self.assertFalse((a == b).all())
def check_equal(self, obj, **kwargs):
a = hash_pandas_object(obj, **kwargs)
b = hash_pandas_object(obj, **kwargs)
tm.assert_series_equal(a, b)
kwargs.pop('index', None)
a = hash_pandas_object(obj, **kwargs)
b = hash_pandas_object(obj, **kwargs)
tm.assert_series_equal(a, b)
def test_hash_keys(self):
# using different hash keys, should have different hashes
# for the same data
# this only matters for object dtypes
obj = Series(list('abc'))
a = hash_pandas_object(obj, hash_key='9876543210123456')
b = hash_pandas_object(obj, hash_key='9876543210123465')
self.assertTrue((a != b).all())
def check_equal(self, obj, **kwargs):
a = hash_pandas_object(obj, **kwargs)
b = hash_pandas_object(obj, **kwargs)
tm.assert_series_equal(a, b)
kwargs.pop('index', None)
a = hash_pandas_object(obj, **kwargs)
b = hash_pandas_object(obj, **kwargs)
tm.assert_series_equal(a, b)
def test_hash_keys(self):
# using different hash keys, should have different hashes
# for the same data
# this only matters for object dtypes
obj = Series(list('abc'))
a = hash_pandas_object(obj, hash_key='9876543210123456')
b = hash_pandas_object(obj, hash_key='9876543210123465')
self.assertTrue((a != b).all())
def test_mixed(self):
# mixed objects
obj = Series(['1', 2, 3])
self.check_equal(obj)
self.check_not_equal_with_index(obj)
# mixed are actually equal when stringified
a = hash_pandas_object(obj)
b = hash_pandas_object(Series(list('123')))
self.assert_series_equal(a, b)
def test_pandas_errors(self):
for obj in [pd.Timestamp('20130101')]:
with pytest.raises(TypeError):
hash_pandas_object(obj)
with catch_warnings(record=True):
obj = tm.makePanel()
with pytest.raises(TypeError):
hash_pandas_object(obj)
def test_pandas_errors(self):
for obj in [pd.Timestamp('20130101')]:
with pytest.raises(TypeError):
hash_pandas_object(obj)
with catch_warnings(record=True):
obj = tm.makePanel()
with pytest.raises(TypeError):
hash_pandas_object(obj)
def test_mixed(self):
# mixed objects
obj = Series(['1', 2, 3])
self.check_equal(obj)
self.check_not_equal_with_index(obj)
# mixed are actually equal when stringified
a = hash_pandas_object(obj)
b = hash_pandas_object(Series(list('123')))
self.assert_series_equal(a, b)
def hash_pandas_object(obj,
index=True,
encoding='utf8',
hash_key=None,
categorize=True):
if hash_key is None:
hash_key = _default_hash_key
def adder(h, hashed_to_add):
h = np.multiply(h, np.uint(3), h)
return np.add(h, hashed_to_add, h)
if isinstance(obj, pd.Index):
h = hash_array(obj.values, encoding, hash_key,
categorize).astype('uint64')
h = pd.Series(h, index=obj, dtype='uint64')
elif isinstance(obj, pd.Series):
h = hash_array(obj.values, encoding, hash_key,
categorize).astype('uint64')
if index:
h = adder(
h,
hash_pandas_object(obj.index,
index=False,
encoding=encoding,
hash_key=hash_key,
categorize=categorize).values)
h = pd.Series(h, index=obj.index, dtype='uint64')
elif isinstance(obj, pd.DataFrame):
cols = obj.iteritems()
first_series = next(cols)[1]
h = hash_array(first_series.values, encoding, hash_key,
categorize).astype('uint64')
for _, col in cols:
h = adder(
h, hash_array(col.values, encoding, hash_key, categorize))
if index:
h = adder(
h,
hash_pandas_object(obj.index,
index=False,
encoding=encoding,
hash_key=hash_key,
categorize=categorize).values)
h = pd.Series(h, index=obj.index, dtype='uint64')
else:
raise TypeError("Unexpected type for hashing %s" % type(obj))
return h
def test_categorical_consistency(self):
# GH15143
# Check that categoricals hash consistent with their values, not codes
# This should work for categoricals of any dtype
for s1 in [Series(['a', 'b', 'c', 'd']),
Series([1000, 2000, 3000, 4000]),
Series(pd.date_range(0, periods=4))]:
s2 = s1.astype('category').cat.set_categories(s1)
s3 = s2.cat.set_categories(list(reversed(s1)))
for categorize in [True, False]:
# These should all hash identically
h1 = hash_pandas_object(s1, categorize=categorize)
h2 = hash_pandas_object(s2, categorize=categorize)
h3 = hash_pandas_object(s3, categorize=categorize)
tm.assert_series_equal(h1, h2)
tm.assert_series_equal(h1, h3)
def test_consistency(self):
# check that our hash doesn't change because of a mistake
# in the actual code; this is the ground truth
result = hash_pandas_object(Index(['foo', 'bar', 'baz']))
expected = Series(np.array([3600424527151052760, 1374399572096150070,
477881037637427054], dtype='uint64'),
index=['foo', 'bar', 'baz'])
tm.assert_series_equal(result, expected)
def test_consistency(self):
# check that our hash doesn't change because of a mistake
# in the actual code; this is the ground truth
result = hash_pandas_object(Index(['foo', 'bar', 'baz']))
expected = Series(np.array([3600424527151052760, 1374399572096150070,
477881037637427054], dtype='uint64'),
index=['foo', 'bar', 'baz'])
tm.assert_series_equal(result, expected)
def test_hash_tuples(self):
tups = [(1, 'one'), (1, 'two'), (2, 'one')]
result = hash_tuples(tups)
expected = hash_pandas_object(MultiIndex.from_tuples(tups)).values
self.assert_numpy_array_equal(result, expected)
result = hash_tuples(tups[0])
self.assertEqual(result, expected[0])
def test_hash_tuples(self):
tups = [(1, 'one'), (1, 'two'), (2, 'one')]
result = hash_tuples(tups)
expected = hash_pandas_object(MultiIndex.from_tuples(tups)).values
tm.assert_numpy_array_equal(result, expected)
result = hash_tuples(tups[0])
self.assertEqual(result, expected[0])
def test_categorical_consistency(self):
# GH15143
# Check that categoricals hash consistent with their values, not codes
# This should work for categoricals of any dtype
for s1 in [
Series(['a', 'b', 'c', 'd']),
Series([1000, 2000, 3000, 4000]),
Series(pd.date_range(0, periods=4))
]:
s2 = s1.astype('category').cat.set_categories(s1)
s3 = s2.cat.set_categories(list(reversed(s1)))
for categorize in [True, False]:
# These should all hash identically
h1 = hash_pandas_object(s1, categorize=categorize)
h2 = hash_pandas_object(s2, categorize=categorize)
h3 = hash_pandas_object(s3, categorize=categorize)
tm.assert_series_equal(h1, h2)
tm.assert_series_equal(h1, h3)
def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None,
categorize=True):
if hash_key is None:
hash_key = _default_hash_key
def adder(h, hashed_to_add):
h = np.multiply(h, np.uint(3), h)
return np.add(h, hashed_to_add, h)
if isinstance(obj, pd.Index):
h = hash_array(obj.values, encoding, hash_key,
categorize).astype('uint64')
h = pd.Series(h, index=obj, dtype='uint64')
elif isinstance(obj, pd.Series):
h = hash_array(obj.values, encoding, hash_key,
categorize).astype('uint64')
if index:
h = adder(h, hash_pandas_object(obj.index,
index=False,
encoding=encoding,
hash_key=hash_key,
categorize=categorize).values)
h = pd.Series(h, index=obj.index, dtype='uint64')
elif isinstance(obj, pd.DataFrame):
cols = obj.iteritems()
first_series = next(cols)[1]
h = hash_array(first_series.values, encoding,
hash_key, categorize).astype('uint64')
for _, col in cols:
h = adder(h, hash_array(col.values, encoding, hash_key,
categorize))
if index:
h = adder(h, hash_pandas_object(obj.index,
index=False,
encoding=encoding,
hash_key=hash_key,
categorize=categorize).values)
h = pd.Series(h, index=obj.index, dtype='uint64')
else:
raise TypeError("Unexpected type for hashing %s" % type(obj))
return h
def f():
hash_pandas_object(f)
def time_frame(self):
hash_pandas_object(self.df)
def time_series_int(self):
hash_pandas_object(self.df.E)
def time_series_string(self):
hash_pandas_object(self.df.B)
def f():
hash_pandas_object(Series(list('abc')), hash_key='foo')
def time_series_categorical(self):
hash_pandas_object(self.df.C)
def test_multiindex_unique(self):
mi = MultiIndex.from_tuples([(118, 472), (236, 118),
(51, 204), (102, 51)])
self.assertTrue(mi.is_unique)
result = hash_pandas_object(mi)
self.assertTrue(result.is_unique)
def test_multiindex_unique(self):
mi = MultiIndex.from_tuples([(118, 472), (236, 118), (51, 204),
(102, 51)])
self.assertTrue(mi.is_unique)
result = hash_pandas_object(mi)
self.assertTrue(result.is_unique)
def f():
hash_pandas_object(f)
def f():
hash_pandas_object(Series(list('abc')), hash_key='foo')
