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Apr 29, 2017
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MAINT: Remove self.assertEqual from testing #16169

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2 changes: 2 additions & 0 deletions pandas/compat/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,6 +104,7 @@ def signature(f):
map = map
zip = zip
filter = filter
intern = sys.intern
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this is fine
but where is it used?

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@gfyoung gfyoung Apr 29, 2017 •
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I stumbled upon it in a test (test_util.py), so I added this to compat for that reason.

reduce = functools.reduce
long = int
unichr = chr
Expand Down Expand Up @@ -146,6 +147,7 @@ def signature(f):

# import iterator versions of these functions
range = xrange
intern = intern
zip = itertools.izip
filter = itertools.ifilter
map = itertools.imap
Expand Down
99 changes: 49 additions & 50 deletions pandas/tests/computation/test_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
from numpy.random import randn, rand, randint
import numpy as np

from pandas.core.dtypes.common import is_list_like, is_scalar
from pandas.core.dtypes.common import is_bool, is_list_like, is_scalar
import pandas as pd
from pandas.core import common as com
from pandas.errors import PerformanceWarning
Expand Down Expand Up @@ -209,7 +209,7 @@ def check_equal(self, result, expected):
elif isinstance(result, np.ndarray):
tm.assert_numpy_array_equal(result, expected)
else:
self.assertEqual(result, expected)
assert result == expected

def check_complex_cmp_op(self, lhs, cmp1, rhs, binop, cmp2):
skip_these = _scalar_skip
Expand Down Expand Up @@ -610,30 +610,28 @@ def test_scalar_unary(self):
with pytest.raises(TypeError):
pd.eval('~1.0', engine=self.engine, parser=self.parser)

self.assertEqual(
pd.eval('-1.0', parser=self.parser, engine=self.engine), -1.0)
self.assertEqual(
pd.eval('+1.0', parser=self.parser, engine=self.engine), +1.0)

self.assertEqual(
pd.eval('~1', parser=self.parser, engine=self.engine), ~1)
self.assertEqual(
pd.eval('-1', parser=self.parser, engine=self.engine), -1)
self.assertEqual(
pd.eval('+1', parser=self.parser, engine=self.engine), +1)

self.assertEqual(
pd.eval('~True', parser=self.parser, engine=self.engine), ~True)
self.assertEqual(
pd.eval('~False', parser=self.parser, engine=self.engine), ~False)
self.assertEqual(
pd.eval('-True', parser=self.parser, engine=self.engine), -True)
self.assertEqual(
pd.eval('-False', parser=self.parser, engine=self.engine), -False)
self.assertEqual(
pd.eval('+True', parser=self.parser, engine=self.engine), +True)
self.assertEqual(
pd.eval('+False', parser=self.parser, engine=self.engine), +False)
assert pd.eval('-1.0', parser=self.parser,
engine=self.engine) == -1.0
assert pd.eval('+1.0', parser=self.parser,
engine=self.engine) == +1.0
assert pd.eval('~1', parser=self.parser,
engine=self.engine) == ~1
assert pd.eval('-1', parser=self.parser,
engine=self.engine) == -1
assert pd.eval('+1', parser=self.parser,
engine=self.engine) == +1
assert pd.eval('~True', parser=self.parser,
engine=self.engine) == ~True
assert pd.eval('~False', parser=self.parser,
engine=self.engine) == ~False
assert pd.eval('-True', parser=self.parser,
engine=self.engine) == -True
assert pd.eval('-False', parser=self.parser,
engine=self.engine) == -False
assert pd.eval('+True', parser=self.parser,
engine=self.engine) == +True
assert pd.eval('+False', parser=self.parser,
engine=self.engine) == +False

def test_unary_in_array(self):
# GH 11235
Expand All @@ -658,50 +656,51 @@ def test_disallow_scalar_bool_ops(self):
pd.eval(ex, engine=self.engine, parser=self.parser)

def test_identical(self):
# GH 10546
# see gh-10546
x = 1
result = pd.eval('x', engine=self.engine, parser=self.parser)
self.assertEqual(result, 1)
assert result == 1
assert is_scalar(result)

x = 1.5
result = pd.eval('x', engine=self.engine, parser=self.parser)
self.assertEqual(result, 1.5)
assert result == 1.5
assert is_scalar(result)

x = False
result = pd.eval('x', engine=self.engine, parser=self.parser)
self.assertEqual(result, False)
assert not result
assert is_bool(result)
assert is_scalar(result)

x = np.array([1])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1]))
self.assertEqual(result.shape, (1, ))
assert result.shape == (1, )

x = np.array([1.5])
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([1.5]))
self.assertEqual(result.shape, (1, ))
assert result.shape == (1, )

x = np.array([False]) # noqa
result = pd.eval('x', engine=self.engine, parser=self.parser)
tm.assert_numpy_array_equal(result, np.array([False]))
self.assertEqual(result.shape, (1, ))
assert result.shape == (1, )

def test_line_continuation(self):
# GH 11149
exp = """1 + 2 * \
5 - 1 + 2 """
result = pd.eval(exp, engine=self.engine, parser=self.parser)
self.assertEqual(result, 12)
assert result == 12

def test_float_truncation(self):
# GH 14241
exp = '1000000000.006'
result = pd.eval(exp, engine=self.engine, parser=self.parser)
expected = np.float64(exp)
self.assertEqual(result, expected)
assert result == expected

df = pd.DataFrame({'A': [1000000000.0009,
1000000000.0011,
Expand Down Expand Up @@ -1121,15 +1120,15 @@ def test_simple_bool_ops(self):
ex = '{0} {1} {2}'.format(lhs, op, rhs)
res = self.eval(ex)
exp = eval(ex)
self.assertEqual(res, exp)
assert res == exp

def test_bool_ops_with_constants(self):
for op, lhs, rhs in product(expr._bool_ops_syms, ('True', 'False'),
('True', 'False')):
ex = '{0} {1} {2}'.format(lhs, op, rhs)
res = self.eval(ex)
exp = eval(ex)
self.assertEqual(res, exp)
assert res == exp

def test_panel_fails(self):
with catch_warnings(record=True):
Expand Down Expand Up @@ -1169,19 +1168,19 @@ def test_truediv(self):

res = self.eval('1 / 2', truediv=True)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec

res = self.eval('1 / 2', truediv=False)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec

res = self.eval('s / 2', truediv=False)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec

res = self.eval('s / 2', truediv=True)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec
else:
res = self.eval(ex, truediv=False)
tm.assert_numpy_array_equal(res, np.array([1]))
Expand All @@ -1191,19 +1190,19 @@ def test_truediv(self):

res = self.eval('1 / 2', truediv=True)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec

res = self.eval('1 / 2', truediv=False)
expec = 0
self.assertEqual(res, expec)
assert res == expec

res = self.eval('s / 2', truediv=False)
expec = 0
self.assertEqual(res, expec)
assert res == expec

res = self.eval('s / 2', truediv=True)
expec = 0.5
self.assertEqual(res, expec)
assert res == expec

def test_failing_subscript_with_name_error(self):
df = DataFrame(np.random.randn(5, 3)) # noqa
Expand Down Expand Up @@ -1549,7 +1548,7 @@ def test_bool_ops_with_constants(self):
else:
res = self.eval(ex)
exp = eval(ex)
self.assertEqual(res, exp)
assert res == exp

def test_simple_bool_ops(self):
for op, lhs, rhs in product(expr._bool_ops_syms, (True, False),
Expand All @@ -1561,7 +1560,7 @@ def test_simple_bool_ops(self):
else:
res = pd.eval(ex, engine=self.engine, parser=self.parser)
exp = eval(ex)
self.assertEqual(res, exp)
assert res == exp


class TestOperationsPythonPython(TestOperationsNumExprPython):
Expand Down Expand Up @@ -1650,14 +1649,14 @@ def test_df_arithmetic_subexpression(self):

def check_result_type(self, dtype, expect_dtype):
df = DataFrame({'a': np.random.randn(10).astype(dtype)})
self.assertEqual(df.a.dtype, dtype)
assert df.a.dtype == dtype
df.eval("b = sin(a)",
engine=self.engine,
parser=self.parser, inplace=True)
got = df.b
expect = np.sin(df.a)
self.assertEqual(expect.dtype, got.dtype)
self.assertEqual(expect_dtype, got.dtype)
assert expect.dtype == got.dtype
assert expect_dtype == got.dtype
tm.assert_series_equal(got, expect, check_names=False)

def test_result_types(self):
Expand Down
45 changes: 22 additions & 23 deletions pandas/tests/dtypes/test_cast.py
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Expand Up @@ -164,7 +164,7 @@ def test_maybe_convert_string_to_array(self):
assert result.dtype == object

result = maybe_convert_string_to_object(1)
self.assertEqual(result, 1)
assert result == 1

arr = np.array(['x', 'y'], dtype=str)
result = maybe_convert_string_to_object(arr)
Expand All @@ -187,31 +187,31 @@ def test_maybe_convert_scalar(self):

# pass thru
result = maybe_convert_scalar('x')
self.assertEqual(result, 'x')
assert result == 'x'
result = maybe_convert_scalar(np.array([1]))
self.assertEqual(result, np.array([1]))
assert result == np.array([1])

# leave scalar dtype
result = maybe_convert_scalar(np.int64(1))
self.assertEqual(result, np.int64(1))
assert result == np.int64(1)
result = maybe_convert_scalar(np.int32(1))
self.assertEqual(result, np.int32(1))
assert result == np.int32(1)
result = maybe_convert_scalar(np.float32(1))
self.assertEqual(result, np.float32(1))
assert result == np.float32(1)
result = maybe_convert_scalar(np.int64(1))
self.assertEqual(result, np.float64(1))
assert result == np.float64(1)

# coerce
result = maybe_convert_scalar(1)
self.assertEqual(result, np.int64(1))
assert result == np.int64(1)
result = maybe_convert_scalar(1.0)
self.assertEqual(result, np.float64(1))
assert result == np.float64(1)
result = maybe_convert_scalar(Timestamp('20130101'))
self.assertEqual(result, Timestamp('20130101').value)
assert result == Timestamp('20130101').value
result = maybe_convert_scalar(datetime(2013, 1, 1))
self.assertEqual(result, Timestamp('20130101').value)
assert result == Timestamp('20130101').value
result = maybe_convert_scalar(Timedelta('1 day 1 min'))
self.assertEqual(result, Timedelta('1 day 1 min').value)
assert result == Timedelta('1 day 1 min').value


class TestConvert(tm.TestCase):
Expand Down Expand Up @@ -291,34 +291,33 @@ def test_numpy_dtypes(self):
((np.dtype('datetime64[ns]'), np.int64), np.object)
)
for src, common in testcases:
self.assertEqual(find_common_type(src), common)
assert find_common_type(src) == common

with pytest.raises(ValueError):
# empty
find_common_type([])

def test_categorical_dtype(self):
dtype = CategoricalDtype()
self.assertEqual(find_common_type([dtype]), 'category')
self.assertEqual(find_common_type([dtype, dtype]), 'category')
self.assertEqual(find_common_type([np.object, dtype]), np.object)
assert find_common_type([dtype]) == 'category'
assert find_common_type([dtype, dtype]) == 'category'
assert find_common_type([np.object, dtype]) == np.object

def test_datetimetz_dtype(self):
dtype = DatetimeTZDtype(unit='ns', tz='US/Eastern')
self.assertEqual(find_common_type([dtype, dtype]),
'datetime64[ns, US/Eastern]')
assert find_common_type([dtype, dtype]) == 'datetime64[ns, US/Eastern]'

for dtype2 in [DatetimeTZDtype(unit='ns', tz='Asia/Tokyo'),
np.dtype('datetime64[ns]'), np.object, np.int64]:
self.assertEqual(find_common_type([dtype, dtype2]), np.object)
self.assertEqual(find_common_type([dtype2, dtype]), np.object)
assert find_common_type([dtype, dtype2]) == np.object
assert find_common_type([dtype2, dtype]) == np.object

def test_period_dtype(self):
dtype = PeriodDtype(freq='D')
self.assertEqual(find_common_type([dtype, dtype]), 'period[D]')
assert find_common_type([dtype, dtype]) == 'period[D]'

for dtype2 in [DatetimeTZDtype(unit='ns', tz='Asia/Tokyo'),
PeriodDtype(freq='2D'), PeriodDtype(freq='H'),
np.dtype('datetime64[ns]'), np.object, np.int64]:
self.assertEqual(find_common_type([dtype, dtype2]), np.object)
self.assertEqual(find_common_type([dtype2, dtype]), np.object)
assert find_common_type([dtype, dtype2]) == np.object
assert find_common_type([dtype2, dtype]) == np.object
16 changes: 8 additions & 8 deletions pandas/tests/dtypes/test_common.py
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Expand Up @@ -30,30 +30,30 @@ def test_invalid_dtype_error(self):

def test_numpy_dtype(self):
for dtype in ['M8[ns]', 'm8[ns]', 'object', 'float64', 'int64']:
self.assertEqual(pandas_dtype(dtype), np.dtype(dtype))
assert pandas_dtype(dtype) == np.dtype(dtype)

def test_numpy_string_dtype(self):
# do not parse freq-like string as period dtype
self.assertEqual(pandas_dtype('U'), np.dtype('U'))
self.assertEqual(pandas_dtype('S'), np.dtype('S'))
assert pandas_dtype('U') == np.dtype('U')
assert pandas_dtype('S') == np.dtype('S')

def test_datetimetz_dtype(self):
for dtype in ['datetime64[ns, US/Eastern]',
'datetime64[ns, Asia/Tokyo]',
'datetime64[ns, UTC]']:
assert pandas_dtype(dtype) is DatetimeTZDtype(dtype)
self.assertEqual(pandas_dtype(dtype), DatetimeTZDtype(dtype))
self.assertEqual(pandas_dtype(dtype), dtype)
assert pandas_dtype(dtype) == DatetimeTZDtype(dtype)
assert pandas_dtype(dtype) == dtype

def test_categorical_dtype(self):
self.assertEqual(pandas_dtype('category'), CategoricalDtype())
assert pandas_dtype('category') == CategoricalDtype()

def test_period_dtype(self):
for dtype in ['period[D]', 'period[3M]', 'period[U]',
'Period[D]', 'Period[3M]', 'Period[U]']:
assert pandas_dtype(dtype) is PeriodDtype(dtype)
self.assertEqual(pandas_dtype(dtype), PeriodDtype(dtype))
self.assertEqual(pandas_dtype(dtype), dtype)
assert pandas_dtype(dtype) == PeriodDtype(dtype)
assert pandas_dtype(dtype) == dtype


dtypes = dict(datetime_tz=pandas_dtype('datetime64[ns, US/Eastern]'),
Expand Down
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