Python pandas.core.base.SpecificationError() Examples
The following are 10
code examples of pandas.core.base.SpecificationError().
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Example #1
| Source File: test_window.py From recruit with Apache License 2.0 | 5 votes |
|
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)
Example #2
| Source File: test_aggregate.py From recruit with Apache License 2.0 | 5 votes |
|
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)
Example #3
| Source File: test_window.py From vnpy_crypto with MIT License | 5 votes |
|
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)
Example #4
| Source File: test_window.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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)
Example #5
| Source File: test_aggregate.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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)
Example #6
| Source File: test_window.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
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)
Example #7
| Source File: test_window.py From coffeegrindsize with MIT License | 5 votes |
|
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)
Example #8
| Source File: test_window.py From twitter-stock-recommendation with MIT License | 5 votes |
|
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)
Example #9
| Source File: generic.py From recruit with Apache License 2.0 | 4 votes |
|
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)
Example #10
| Source File: generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
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)
