Python pandas.util.testing.assert_categorical_equal() Examples
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code examples of pandas.util.testing.assert_categorical_equal().
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Example #1
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
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def test_shift(self):
# GH 9416
cat = Categorical(['a', 'b', 'c', 'd', 'a'])
# shift forward
sp1 = cat.shift(1)
xp1 = Categorical([np.nan, 'a', 'b', 'c', 'd'])
tm.assert_categorical_equal(sp1, xp1)
tm.assert_categorical_equal(cat[:-1], sp1[1:])
# shift back
sn2 = cat.shift(-2)
xp2 = Categorical(['c', 'd', 'a', np.nan, np.nan],
categories=['a', 'b', 'c', 'd'])
tm.assert_categorical_equal(sn2, xp2)
tm.assert_categorical_equal(cat[2:], sn2[:-2])
# shift by zero
tm.assert_categorical_equal(cat, cat.shift(0))
Example #2
| Source File: test_indexing.py From recruit with Apache License 2.0 | 6 votes |
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def test_setitem(self):
# int/positional
c = self.factor.copy()
c[0] = 'b'
assert c[0] == 'b'
c[-1] = 'a'
assert c[-1] == 'a'
# boolean
c = self.factor.copy()
indexer = np.zeros(len(c), dtype='bool')
indexer[0] = True
indexer[-1] = True
c[indexer] = 'c'
expected = Categorical(['c', 'b', 'b', 'a', 'a', 'c', 'c', 'c'],
ordered=True)
tm.assert_categorical_equal(c, expected)
Example #3
| Source File: test_union_categoricals.py From recruit with Apache License 2.0 | 6 votes |
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def test_union_categoricals_ordered(self):
c1 = Categorical([1, 2, 3], ordered=True)
c2 = Categorical([1, 2, 3], ordered=False)
msg = 'Categorical.ordered must be the same'
with pytest.raises(TypeError, match=msg):
union_categoricals([c1, c2])
res = union_categoricals([c1, c1])
exp = Categorical([1, 2, 3, 1, 2, 3], ordered=True)
tm.assert_categorical_equal(res, exp)
c1 = Categorical([1, 2, 3, np.nan], ordered=True)
c2 = Categorical([3, 2], categories=[1, 2, 3], ordered=True)
res = union_categoricals([c1, c2])
exp = Categorical([1, 2, 3, np.nan, 3, 2], ordered=True)
tm.assert_categorical_equal(res, exp)
c1 = Categorical([1, 2, 3], ordered=True)
c2 = Categorical([1, 2, 3], categories=[3, 2, 1], ordered=True)
msg = "to union ordered Categoricals, all categories must be the same"
with pytest.raises(TypeError, match=msg):
union_categoricals([c1, c2])
Example #4
| Source File: test_union_categoricals.py From recruit with Apache License 2.0 | 6 votes |
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def test_union_categorical_unwrap(self):
# GH 14173
c1 = Categorical(['a', 'b'])
c2 = pd.Series(['b', 'c'], dtype='category')
result = union_categoricals([c1, c2])
expected = Categorical(['a', 'b', 'b', 'c'])
tm.assert_categorical_equal(result, expected)
c2 = CategoricalIndex(c2)
result = union_categoricals([c1, c2])
tm.assert_categorical_equal(result, expected)
c1 = Series(c1)
result = union_categoricals([c1, c2])
tm.assert_categorical_equal(result, expected)
with pytest.raises(TypeError):
union_categoricals([c1, ['a', 'b', 'c']])
Example #5
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
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def test_describe_categorical_columns(self):
# GH 11558
columns = pd.CategoricalIndex(['int1', 'int2', 'obj'],
ordered=True, name='XXX')
df = DataFrame({'int1': [10, 20, 30, 40, 50],
'int2': [10, 20, 30, 40, 50],
'obj': ['A', 0, None, 'X', 1]},
columns=columns)
result = df.describe()
exp_columns = pd.CategoricalIndex(['int1', 'int2'],
categories=['int1', 'int2', 'obj'],
ordered=True, name='XXX')
expected = DataFrame({'int1': [5, 30, df.int1.std(),
10, 20, 30, 40, 50],
'int2': [5, 30, df.int2.std(),
10, 20, 30, 40, 50]},
index=['count', 'mean', 'std', 'min', '25%',
'50%', '75%', 'max'],
columns=exp_columns)
tm.assert_frame_equal(result, expected)
tm.assert_categorical_equal(result.columns.values,
expected.columns.values)
Example #6
| Source File: test_dtypes.py From recruit with Apache License 2.0 | 6 votes |
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def test_astype_category(self, dtype_ordered, cat_ordered):
# GH 10696/18593
data = list('abcaacbab')
cat = Categorical(data, categories=list('bac'), ordered=cat_ordered)
# standard categories
dtype = CategoricalDtype(ordered=dtype_ordered)
result = cat.astype(dtype)
expected = Categorical(
data, categories=cat.categories, ordered=dtype_ordered)
tm.assert_categorical_equal(result, expected)
# non-standard categories
dtype = CategoricalDtype(list('adc'), dtype_ordered)
result = cat.astype(dtype)
expected = Categorical(data, dtype=dtype)
tm.assert_categorical_equal(result, expected)
if dtype_ordered is False:
# dtype='category' can't specify ordered, so only test once
result = cat.astype('category')
expected = cat
tm.assert_categorical_equal(result, expected)
Example #7
| Source File: test_categorical.py From recruit with Apache License 2.0 | 6 votes |
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def test_unstack_categorical():
# GH11558 (example is taken from the original issue)
df = pd.DataFrame({'a': range(10),
'medium': ['A', 'B'] * 5,
'artist': list('XYXXY') * 2})
df['medium'] = df['medium'].astype('category')
gcat = df.groupby(
['artist', 'medium'], observed=False)['a'].count().unstack()
result = gcat.describe()
exp_columns = pd.CategoricalIndex(['A', 'B'], ordered=False,
name='medium')
tm.assert_index_equal(result.columns, exp_columns)
tm.assert_categorical_equal(result.columns.values, exp_columns.values)
result = gcat['A'] + gcat['B']
expected = pd.Series([6, 4], index=pd.Index(['X', 'Y'], name='artist'))
tm.assert_series_equal(result, expected)
Example #8
| Source File: test_constructors.py From recruit with Apache License 2.0 | 6 votes |
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def test_constructor_with_generator(self):
# This was raising an Error in isna(single_val).any() because isna
# returned a scalar for a generator
xrange = range
exp = Categorical([0, 1, 2])
cat = Categorical((x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical(xrange(3))
tm.assert_categorical_equal(cat, exp)
# This uses xrange internally
from pandas.core.index import MultiIndex
MultiIndex.from_product([range(5), ['a', 'b', 'c']])
# check that categories accept generators and sequences
cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
tm.assert_categorical_equal(cat, exp)
cat = Categorical([0, 1, 2], categories=xrange(3))
tm.assert_categorical_equal(cat, exp)
Example #9
| Source File: test_apply.py From recruit with Apache License 2.0 | 6 votes |
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def test_map_categorical(self):
values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'),
ordered=True)
s = pd.Series(values, name='XX', index=list('abcdefg'))
result = s.map(lambda x: x.lower())
exp_values = pd.Categorical(list('abbabcd'), categories=list('dcba'),
ordered=True)
exp = pd.Series(exp_values, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
tm.assert_categorical_equal(result.values, exp_values)
result = s.map(lambda x: 'A')
exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
assert result.dtype == np.object
with pytest.raises(NotImplementedError):
s.map(lambda x: x, na_action='ignore')
Example #10
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
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def test_unique_index_series(self):
c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1])
# Categorical.unique sorts categories by appearance order
# if ordered=False
exp = Categorical([3, 1, 2], categories=[3, 1, 2])
tm.assert_categorical_equal(c.unique(), exp)
tm.assert_index_equal(Index(c).unique(), Index(exp))
tm.assert_categorical_equal(Series(c).unique(), exp)
c = Categorical([1, 1, 2, 2], categories=[3, 2, 1])
exp = Categorical([1, 2], categories=[1, 2])
tm.assert_categorical_equal(c.unique(), exp)
tm.assert_index_equal(Index(c).unique(), Index(exp))
tm.assert_categorical_equal(Series(c).unique(), exp)
c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1], ordered=True)
# Categorical.unique keeps categories order if ordered=True
exp = Categorical([3, 1, 2], categories=[3, 2, 1], ordered=True)
tm.assert_categorical_equal(c.unique(), exp)
tm.assert_index_equal(Index(c).unique(), Index(exp))
tm.assert_categorical_equal(Series(c).unique(), exp)
Example #11
| Source File: test_qcut.py From recruit with Apache License 2.0 | 6 votes |
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def test_qcut():
arr = np.random.randn(1000)
# We store the bins as Index that have been
# rounded to comparisons are a bit tricky.
labels, bins = qcut(arr, 4, retbins=True)
ex_bins = quantile(arr, [0, .25, .5, .75, 1.])
result = labels.categories.left.values
assert np.allclose(result, ex_bins[:-1], atol=1e-2)
result = labels.categories.right.values
assert np.allclose(result, ex_bins[1:], atol=1e-2)
ex_levels = cut(arr, ex_bins, include_lowest=True)
tm.assert_categorical_equal(labels, ex_levels)
Example #12
| Source File: test_analytics.py From recruit with Apache License 2.0 | 6 votes |
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def test_apply_categorical(self):
values = pd.Categorical(list('ABBABCD'), categories=list('DCBA'),
ordered=True)
s = pd.Series(values, name='XX', index=list('abcdefg'))
result = s.apply(lambda x: x.lower())
# should be categorical dtype when the number of categories are
# the same
values = pd.Categorical(list('abbabcd'), categories=list('dcba'),
ordered=True)
exp = pd.Series(values, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
tm.assert_categorical_equal(result.values, exp.values)
result = s.apply(lambda x: 'A')
exp = pd.Series(['A'] * 7, name='XX', index=list('abcdefg'))
tm.assert_series_equal(result, exp)
assert result.dtype == np.object
Example #13
| Source File: test_api.py From recruit with Apache License 2.0 | 6 votes |
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def test_remove_categories(self):
cat = Categorical(["a", "b", "c", "a"], ordered=True)
old = cat.copy()
new = Categorical(["a", "b", np.nan, "a"], categories=["a", "b"],
ordered=True)
# first inplace == False
res = cat.remove_categories("c")
tm.assert_categorical_equal(cat, old)
tm.assert_categorical_equal(res, new)
res = cat.remove_categories(["c"])
tm.assert_categorical_equal(cat, old)
tm.assert_categorical_equal(res, new)
# inplace == True
res = cat.remove_categories("c", inplace=True)
tm.assert_categorical_equal(cat, new)
assert res is None
# removal is not in categories
with pytest.raises(ValueError):
cat.remove_categories(["c"])
Example #14
| Source File: test_cut.py From recruit with Apache License 2.0 | 5 votes |
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def test_no_right():
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
result, bins = cut(data, 4, right=False, retbins=True)
intervals = IntervalIndex.from_breaks(bins.round(3), closed="left")
intervals = intervals.take([0, 0, 0, 2, 3, 0, 1])
expected = Categorical(intervals, ordered=True)
tm.assert_categorical_equal(result, expected)
tm.assert_almost_equal(bins, np.array([0.2, 2.575, 4.95, 7.325, 9.7095]))
Example #15
| Source File: test_cut.py From recruit with Apache License 2.0 | 5 votes |
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def test_right():
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1, 2.575])
result, bins = cut(data, 4, right=True, retbins=True)
intervals = IntervalIndex.from_breaks(bins.round(3))
expected = Categorical(intervals, ordered=True)
expected = expected.take([0, 0, 0, 2, 3, 0, 0])
tm.assert_categorical_equal(result, expected)
tm.assert_almost_equal(bins, np.array([0.1905, 2.575, 4.95, 7.325, 9.7]))
Example #16
| Source File: test_common.py From recruit with Apache License 2.0 | 5 votes |
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def test_read_fspath_all(self, reader, module, path, datapath):
pytest.importorskip(module)
path = datapath(*path)
mypath = CustomFSPath(path)
result = reader(mypath)
expected = reader(path)
if path.endswith('.pickle'):
# categorical
tm.assert_categorical_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
Example #17
| Source File: test_construct_from_scalar.py From recruit with Apache License 2.0 | 5 votes |
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def test_cast_1d_array_like_from_scalar_categorical():
# see gh-19565
#
# Categorical result from scalar did not maintain
# categories and ordering of the passed dtype.
cats = ["a", "b", "c"]
cat_type = CategoricalDtype(categories=cats, ordered=False)
expected = Categorical(["a", "a"], categories=cats)
result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type)
tm.assert_categorical_equal(result, expected,
check_category_order=True,
check_dtype=True)
Example #18
| Source File: test_algos.py From recruit with Apache License 2.0 | 5 votes |
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def test_order_of_appearance(self):
# 9346
# light testing of guarantee of order of appearance
# these also are the doc-examples
result = pd.unique(Series([2, 1, 3, 3]))
tm.assert_numpy_array_equal(result,
np.array([2, 1, 3], dtype='int64'))
result = pd.unique(Series([2] + [1] * 5))
tm.assert_numpy_array_equal(result,
np.array([2, 1], dtype='int64'))
result = pd.unique(Series([Timestamp('20160101'),
Timestamp('20160101')]))
expected = np.array(['2016-01-01T00:00:00.000000000'],
dtype='datetime64[ns]')
tm.assert_numpy_array_equal(result, expected)
result = pd.unique(Index(
[Timestamp('20160101', tz='US/Eastern'),
Timestamp('20160101', tz='US/Eastern')]))
expected = DatetimeIndex(['2016-01-01 00:00:00'],
dtype='datetime64[ns, US/Eastern]',
freq=None)
tm.assert_index_equal(result, expected)
result = pd.unique(list('aabc'))
expected = np.array(['a', 'b', 'c'], dtype=object)
tm.assert_numpy_array_equal(result, expected)
result = pd.unique(Series(Categorical(list('aabc'))))
expected = Categorical(list('abc'))
tm.assert_categorical_equal(result, expected)
Example #19
| Source File: test_algos.py From recruit with Apache License 2.0 | 5 votes |
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def test_categorical(self):
c = Categorical([1, 2])
exp = c
tm.assert_categorical_equal(algos.mode(c), exp)
tm.assert_categorical_equal(c.mode(), exp)
c = Categorical([1, 'a', 'a'])
exp = Categorical(['a'], categories=[1, 'a'])
tm.assert_categorical_equal(algos.mode(c), exp)
tm.assert_categorical_equal(c.mode(), exp)
c = Categorical([1, 1, 2, 3, 3])
exp = Categorical([1, 3], categories=[1, 2, 3])
tm.assert_categorical_equal(algos.mode(c), exp)
tm.assert_categorical_equal(c.mode(), exp)
Example #20
| Source File: test_categorical.py From recruit with Apache License 2.0 | 5 votes |
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def test_describe_categorical_columns():
# GH 11558
cats = pd.CategoricalIndex(['qux', 'foo', 'baz', 'bar'],
categories=['foo', 'bar', 'baz', 'qux'],
ordered=True)
df = DataFrame(np.random.randn(20, 4), columns=cats)
result = df.groupby([1, 2, 3, 4] * 5).describe()
tm.assert_index_equal(result.stack().columns, cats)
tm.assert_categorical_equal(result.stack().columns.values, cats.values)
Example #21
| Source File: test_union_categoricals.py From recruit with Apache License 2.0 | 5 votes |
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def test_union_categorical_same_category(self):
# check fastpath
c1 = Categorical([1, 2, 3, 4], categories=[1, 2, 3, 4])
c2 = Categorical([3, 2, 1, np.nan], categories=[1, 2, 3, 4])
res = union_categoricals([c1, c2])
exp = Categorical([1, 2, 3, 4, 3, 2, 1, np.nan],
categories=[1, 2, 3, 4])
tm.assert_categorical_equal(res, exp)
c1 = Categorical(['z', 'z', 'z'], categories=['x', 'y', 'z'])
c2 = Categorical(['x', 'x', 'x'], categories=['x', 'y', 'z'])
res = union_categoricals([c1, c2])
exp = Categorical(['z', 'z', 'z', 'x', 'x', 'x'],
categories=['x', 'y', 'z'])
tm.assert_categorical_equal(res, exp)
Example #22
| Source File: test_cut.py From recruit with Apache License 2.0 | 5 votes |
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def test_bins():
data = np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1])
result, bins = cut(data, 3, retbins=True)
intervals = IntervalIndex.from_breaks(bins.round(3))
intervals = intervals.take([0, 0, 0, 1, 2, 0])
expected = Categorical(intervals, ordered=True)
tm.assert_categorical_equal(result, expected)
tm.assert_almost_equal(bins, np.array([0.1905, 3.36666667,
6.53333333, 9.7]))
Example #23
| Source File: test_qcut.py From recruit with Apache License 2.0 | 5 votes |
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def test_qcut_index():
result = qcut([0, 2], 2)
intervals = [Interval(-0.001, 1), Interval(1, 2)]
expected = Categorical(intervals, ordered=True)
tm.assert_categorical_equal(result, expected)
Example #24
| Source File: test_astype.py From recruit with Apache License 2.0 | 5 votes |
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def test_astype_category(self):
obj = pd.period_range("2000", periods=2)
result = obj.astype('category')
expected = pd.CategoricalIndex([pd.Period('2000-01-01', freq="D"),
pd.Period('2000-01-02', freq="D")])
tm.assert_index_equal(result, expected)
result = obj._data.astype('category')
expected = expected.values
tm.assert_categorical_equal(result, expected)
Example #25
| Source File: test_astype.py From recruit with Apache License 2.0 | 5 votes |
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def test_astype_category(self, tz):
obj = pd.date_range("2000", periods=2, tz=tz)
result = obj.astype('category')
expected = pd.CategoricalIndex([pd.Timestamp('2000-01-01', tz=tz),
pd.Timestamp('2000-01-02', tz=tz)])
tm.assert_index_equal(result, expected)
result = obj._data.astype('category')
expected = expected.values
tm.assert_categorical_equal(result, expected)
Example #26
| Source File: test_category.py From recruit with Apache License 2.0 | 5 votes |
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def test_map_with_nan(self, data, f): # GH 24241
values = pd.Categorical(data)
result = values.map(f)
if data[1] == 1:
expected = pd.Categorical([False, False, np.nan])
tm.assert_categorical_equal(result, expected)
else:
expected = pd.Index([False, False, np.nan])
tm.assert_index_equal(result, expected)
Example #27
| Source File: test_astype.py From recruit with Apache License 2.0 | 5 votes |
|
def test_astype_category(self):
obj = pd.timedelta_range("1H", periods=2, freq='H')
result = obj.astype('category')
expected = pd.CategoricalIndex([pd.Timedelta('1H'),
pd.Timedelta('2H')])
tm.assert_index_equal(result, expected)
result = obj._data.astype('category')
expected = expected.values
tm.assert_categorical_equal(result, expected)
Example #28
| Source File: test_constructors.py From recruit with Apache License 2.0 | 5 votes |
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def test_from_inferred_categories_coerces(self):
cats = ['1', '2', 'bad']
codes = np.array([0, 0, 1, 2], dtype='i8')
dtype = CategoricalDtype([1, 2])
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical([1, 1, 2, np.nan])
tm.assert_categorical_equal(result, expected)
Example #29
| Source File: test_constructors.py From recruit with Apache License 2.0 | 5 votes |
|
def test_from_inferred_categories_dtype(self):
cats = ['a', 'b', 'd']
codes = np.array([0, 1, 0, 2], dtype='i8')
dtype = CategoricalDtype(['c', 'b', 'a'], ordered=True)
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical(['a', 'b', 'a', 'd'],
categories=['c', 'b', 'a'],
ordered=True)
tm.assert_categorical_equal(result, expected)
Example #30
| Source File: test_constructors.py From recruit with Apache License 2.0 | 5 votes |
|
def test_from_inferred_categories_sorts(self, dtype):
cats = ['b', 'a']
codes = np.array([0, 1, 1, 1], dtype='i8')
result = Categorical._from_inferred_categories(cats, codes, dtype)
expected = Categorical.from_codes([1, 0, 0, 0], ['a', 'b'])
tm.assert_categorical_equal(result, expected)
