本文整理汇总了Python中pandas.core.base.SpecificationError方法的典型用法代码示例。如果您正苦于以下问题：Python base.SpecificationError方法的具体用法？Python base.SpecificationError怎么用？Python base.SpecificationError使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandas.core.base的用法示例。
在下文中一共展示了base.SpecificationError方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_agg_nested_dicts
# 需要导入模块: from pandas.core import base [as 别名]
# 或者: from pandas.core.base import SpecificationError [as 别名]
def test_agg_nested_dicts(self):
# API change for disallowing these types of nested dicts
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
def f():
r.aggregate({'r1': {'A': ['mean', 'sum']},
'r2': {'B': ['mean', 'sum']}})
pytest.raises(SpecificationError, f)
expected = concat([r['A'].mean(), r['A'].std(),
r['B'].mean(), r['B'].std()], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
with catch_warnings(record=True):
warnings.simplefilter("ignore", FutureWarning)
result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
warnings.simplefilter("ignore", FutureWarning)
result = r.agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
tm.assert_frame_equal(result, expected, check_like=True) 示例2: test_agg_multiple_functions_too_many_lambdas
# 需要导入模块: from pandas.core import base [as 别名]
# 或者: from pandas.core.base import SpecificationError [as 别名]
def test_agg_multiple_functions_too_many_lambdas(df):
grouped = df.groupby('A')
funcs = ['mean', lambda x: x.mean(), lambda x: x.std()]
msg = 'Function names must be unique, found multiple named <lambda>'
with pytest.raises(SpecificationError, match=msg):
grouped.agg(funcs) 示例3: test_agg_nested_dicts
# 需要导入模块: from pandas.core import base [as 别名]
# 或者: from pandas.core.base import SpecificationError [as 别名]
def test_agg_nested_dicts(self):
# API change for disallowing these types of nested dicts
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
def f():
r.aggregate({'r1': {'A': ['mean', 'sum']},
'r2': {'B': ['mean', 'sum']}})
pytest.raises(SpecificationError, f)
expected = concat([r['A'].mean(), r['A'].std(),
r['B'].mean(), r['B'].std()], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
with catch_warnings(record=True):
result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
result = r.agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
tm.assert_frame_equal(result, expected, check_like=True) 示例4: test_agg_nested_dicts
# 需要导入模块: from pandas.core import base [as 别名]
# 或者: from pandas.core.base import SpecificationError [as 别名]
def test_agg_nested_dicts(self):
# API change for disallowing these types of nested dicts
df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
r = df.rolling(window=3)
def f():
r.aggregate({'r1': {'A': ['mean', 'sum']},
'r2': {'B': ['mean', 'sum']}})
pytest.raises(SpecificationError, f)
expected = pd.concat([r['A'].mean(), r['A'].std(), r['B'].mean(),
r['B'].std()], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
with catch_warnings(record=True):
result = r[['A', 'B']].agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
tm.assert_frame_equal(result, expected, check_like=True)
with catch_warnings(record=True):
result = r.agg({'A': {'ra': ['mean', 'std']},
'B': {'rb': ['mean', 'std']}})
expected.columns = pd.MultiIndex.from_tuples([('A', 'ra', 'mean'), (
'A', 'ra', 'std'), ('B', 'rb', 'mean'), ('B', 'rb', 'std')])
tm.assert_frame_equal(result, expected, check_like=True) 示例5: _aggregate_multiple_funcs
# 需要导入模块: from pandas.core import base [as 别名]
# 或者: from pandas.core.base import SpecificationError [as 别名]
def _aggregate_multiple_funcs(self, arg, _level):
if isinstance(arg, dict):
# show the deprecation, but only if we
# have not shown a higher level one
# GH 15931
if isinstance(self._selected_obj, Series) and _level <= 1:
warnings.warn(
("using a dict on a Series for aggregation\n"
"is deprecated and will be removed in a future "
"version"),
FutureWarning, stacklevel=3)
columns = list(arg.keys())
arg = list(arg.items())
elif any(isinstance(x, (tuple, list)) for x in arg):
arg = [(x, x) if not isinstance(x, (tuple, list)) else x
for x in arg]
# indicated column order
columns = lzip(*arg)[0]
else:
# list of functions / function names
columns = []
for f in arg:
if isinstance(f, compat.string_types):
columns.append(f)
else:
# protect against callables without names
columns.append(com.get_callable_name(f))
arg = lzip(columns, arg)
results = {}
for name, func in arg:
obj = self
if name in results:
raise SpecificationError(
'Function names must be unique, found multiple named '
'{}'.format(name))
# reset the cache so that we
# only include the named selection
if name in self._selected_obj:
obj = copy.copy(obj)
obj._reset_cache()
obj._selection = name
results[name] = obj.aggregate(func)
if any(isinstance(x, DataFrame) for x in compat.itervalues(results)):
# let higher level handle
if _level:
return results
return DataFrame(results, columns=columns)