def _aggregate_multiple_funcs(self, arg, _level, _axis):
from pandas.core.reshape.concat import concat
if _axis != 0:
raise NotImplementedError("axis other than 0 is not supported")
if self._selected_obj.ndim == 1:
obj = self._selected_obj
else:
obj = self._obj_with_exclusions
results = []
keys = []
# degenerate case
if obj.ndim == 1:
for a in arg:
try:
colg = self._gotitem(obj.name, ndim=1, subset=obj)
results.append(colg.aggregate(a))
# make sure we find a good name
name = com._get_callable_name(a) or a
keys.append(name)
except (TypeError, DataError):
pass
except SpecificationError:
raise
# multiples
else:
for col in obj:
try:
colg = self._gotitem(col, ndim=1, subset=obj[col])
results.append(colg.aggregate(arg))
keys.append(col)
except (TypeError, DataError):
pass
except ValueError:
# cannot aggregate
continue
except SpecificationError:
raise
# if we are empty
if not len(results):
raise ValueError("no results")
try:
return concat(results, keys=keys, axis=1)
except TypeError:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas.core.dtypes.cast import is_nested_object
from pandas import Series
result = Series(results, index=keys, name=self.name)
if is_nested_object(result):
raise ValueError("cannot combine transform and "
"aggregation operations")
return result
def _aggregate_multiple_funcs(self, arg, _level, _axis):
from pandas.core.reshape.concat import concat
if _axis != 0:
raise NotImplementedError("axis other than 0 is not supported")
if self._selected_obj.ndim == 1:
obj = self._selected_obj
else:
obj = self._obj_with_exclusions
results = []
keys = []
# degenerate case
if obj.ndim == 1:
for a in arg:
try:
colg = self._gotitem(obj.name, ndim=1, subset=obj)
results.append(colg.aggregate(a))
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
except (TypeError, DataError):
pass
except SpecificationError:
raise
# multiples
else:
for index, col in enumerate(obj):
try:
colg = self._gotitem(col,
ndim=1,
subset=obj.iloc[:, index])
results.append(colg.aggregate(arg))
keys.append(col)
except (TypeError, DataError):
pass
except ValueError:
# cannot aggregate
continue
except SpecificationError:
raise
# if we are empty
if not len(results):
raise ValueError("no results")
try:
return concat(results, keys=keys, axis=1, sort=False)
except TypeError:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas.core.dtypes.cast import is_nested_object
from pandas import Series
result = Series(results, index=keys, name=self.name)
if is_nested_object(result):
raise ValueError("cannot combine transform and "
"aggregation operations")
return result
def _aggregate_multiple_funcs(self, arg, _axis):
from pandas.core.reshape.concat import concat
if _axis != 0:
raise NotImplementedError("axis other than 0 is not supported")
if self._selected_obj.ndim == 1:
obj = self._selected_obj
else:
obj = self._obj_with_exclusions
results = []
keys = []
# degenerate case
if obj.ndim == 1:
for a in arg:
colg = self._gotitem(obj.name, ndim=1, subset=obj)
try:
new_res = colg.aggregate(a)
except TypeError:
pass
else:
results.append(new_res)
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
# multiples
else:
for index, col in enumerate(obj):
colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index])
try:
new_res = colg.aggregate(arg)
except (TypeError, DataError):
pass
except ValueError as err:
# cannot aggregate
if "Must produce aggregated value" in str(err):
# raised directly in _aggregate_named
pass
elif "no results" in str(err):
# raised direcly in _aggregate_multiple_funcs
pass
else:
raise
else:
results.append(new_res)
keys.append(col)
# if we are empty
if not len(results):
raise ValueError("no results")
try:
return concat(results, keys=keys, axis=1, sort=False)
except TypeError as err:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas import Series
result = Series(results, index=keys, name=self.name)
if is_nested_object(result):
raise ValueError(
"cannot combine transform and aggregation operations"
) from err
return result
def agg_list_like(self) -> DataFrame | Series:
"""
Compute aggregation in the case of a list-like argument.
Returns
-------
Result of aggregation.
"""
from pandas.core.reshape.concat import concat
obj = self.obj
arg = cast(List[AggFuncTypeBase], self.f)
if not isinstance(obj, SelectionMixin):
# i.e. obj is Series or DataFrame
selected_obj = obj
elif obj._selected_obj.ndim == 1:
# For SeriesGroupBy this matches _obj_with_exclusions
selected_obj = obj._selected_obj
else:
selected_obj = obj._obj_with_exclusions
results = []
keys = []
failed_names = []
depr_nuisance_columns_msg = (
"{} did not aggregate successfully. If any error is "
"raised this will raise in a future version of pandas. "
"Drop these columns/ops to avoid this warning.")
# degenerate case
if selected_obj.ndim == 1:
for a in arg:
colg = obj._gotitem(selected_obj.name,
ndim=1,
subset=selected_obj)
try:
new_res = colg.aggregate(a)
except TypeError:
failed_names.append(com.get_callable_name(a) or a)
else:
results.append(new_res)
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
# multiples
else:
indices = []
for index, col in enumerate(selected_obj):
colg = obj._gotitem(col,
ndim=1,
subset=selected_obj.iloc[:, index])
try:
# Capture and suppress any warnings emitted by us in the call
# to agg below, but pass through any warnings that were
# generated otherwise.
with warnings.catch_warnings(record=True) as record:
new_res = colg.aggregate(arg)
if len(record) > 0:
match = re.compile(
depr_nuisance_columns_msg.format(".*"))
for warning in record:
if re.match(match, str(warning.message)):
failed_names.append(col)
else:
warnings.warn_explicit(
message=warning.message,
category=warning.category,
filename=warning.filename,
lineno=warning.lineno,
)
except (TypeError, DataError):
failed_names.append(col)
except ValueError as err:
# cannot aggregate
if "Must produce aggregated value" in str(err):
# raised directly in _aggregate_named
failed_names.append(col)
elif "no results" in str(err):
# reached in test_frame_apply.test_nuiscance_columns
# where the colg.aggregate(arg) ends up going through
# the selected_obj.ndim == 1 branch above with arg == ["sum"]
# on a datetime64[ns] column
failed_names.append(col)
else:
raise
else:
results.append(new_res)
indices.append(index)
keys = selected_obj.columns.take(indices)
# if we are empty
if not len(results):
raise ValueError("no results")
if len(failed_names) > 0:
warnings.warn(
depr_nuisance_columns_msg.format(failed_names),
FutureWarning,
stacklevel=find_stack_level(),
)
try:
concatenated = concat(results, keys=keys, axis=1, sort=False)
except TypeError as err:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas import Series
result = Series(results, index=keys, name=obj.name)
if is_nested_object(result):
raise ValueError(
"cannot combine transform and aggregation operations"
) from err
return result
else:
# Concat uses the first index to determine the final indexing order.
# The union of a shorter first index with the other indices causes
# the index sorting to be different from the order of the aggregating
# functions. Reindex if this is the case.
index_size = concatenated.index.size
full_ordered_index = next(result.index for result in results
if result.index.size == index_size)
return concatenated.reindex(full_ordered_index, copy=False)
def agg_list_like(self, _axis: int) -> FrameOrSeriesUnion:
"""
Compute aggregation in the case of a list-like argument.
Parameters
----------
_axis : int, 0 or 1
Axis to compute aggregation on.
Returns
-------
Result of aggregation.
"""
from pandas.core.reshape.concat import concat
obj = self.obj
arg = cast(List[AggFuncTypeBase], self.f)
if _axis != 0:
raise NotImplementedError("axis other than 0 is not supported")
if obj._selected_obj.ndim == 1:
selected_obj = obj._selected_obj
else:
selected_obj = obj._obj_with_exclusions
results = []
keys = []
# degenerate case
if selected_obj.ndim == 1:
for a in arg:
colg = obj._gotitem(selected_obj.name,
ndim=1,
subset=selected_obj)
try:
new_res = colg.aggregate(a)
except TypeError:
pass
else:
results.append(new_res)
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
# multiples
else:
for index, col in enumerate(selected_obj):
colg = obj._gotitem(col,
ndim=1,
subset=selected_obj.iloc[:, index])
try:
new_res = colg.aggregate(arg)
except (TypeError, DataError):
pass
except ValueError as err:
# cannot aggregate
if "Must produce aggregated value" in str(err):
# raised directly in _aggregate_named
pass
elif "no results" in str(err):
# raised directly in _aggregate_multiple_funcs
pass
else:
raise
else:
results.append(new_res)
keys.append(col)
# if we are empty
if not len(results):
raise ValueError("no results")
try:
return concat(results, keys=keys, axis=1, sort=False)
except TypeError as err:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas import Series
result = Series(results, index=keys, name=obj.name)
if is_nested_object(result):
raise ValueError(
"cannot combine transform and aggregation operations"
) from err
return result
def agg_list_like(self) -> DataFrame | Series:
"""
Compute aggregation in the case of a list-like argument.
Returns
-------
Result of aggregation.
"""
from pandas.core.reshape.concat import concat
obj = self.obj
arg = cast(List[AggFuncTypeBase], self.f)
if not isinstance(obj, SelectionMixin):
# i.e. obj is Series or DataFrame
selected_obj = obj
elif obj._selected_obj.ndim == 1:
# For SeriesGroupBy this matches _obj_with_exclusions
selected_obj = obj._selected_obj
else:
selected_obj = obj._obj_with_exclusions
results = []
keys = []
# degenerate case
if selected_obj.ndim == 1:
for a in arg:
colg = obj._gotitem(selected_obj.name,
ndim=1,
subset=selected_obj)
try:
new_res = colg.aggregate(a)
except TypeError:
pass
else:
results.append(new_res)
# make sure we find a good name
name = com.get_callable_name(a) or a
keys.append(name)
# multiples
else:
indices = []
for index, col in enumerate(selected_obj):
colg = obj._gotitem(col,
ndim=1,
subset=selected_obj.iloc[:, index])
try:
new_res = colg.aggregate(arg)
except (TypeError, DataError):
pass
except ValueError as err:
# cannot aggregate
if "Must produce aggregated value" in str(err):
# raised directly in _aggregate_named
pass
elif "no results" in str(err):
# reached in test_frame_apply.test_nuiscance_columns
# where the colg.aggregate(arg) ends up going through
# the selected_obj.ndim == 1 branch above with arg == ["sum"]
# on a datetime64[ns] column
pass
else:
raise
else:
results.append(new_res)
indices.append(index)
keys = selected_obj.columns.take(indices)
# if we are empty
if not len(results):
raise ValueError("no results")
try:
concatenated = concat(results, keys=keys, axis=1, sort=False)
except TypeError as err:
# we are concatting non-NDFrame objects,
# e.g. a list of scalars
from pandas import Series
result = Series(results, index=keys, name=obj.name)
if is_nested_object(result):
raise ValueError(
"cannot combine transform and aggregation operations"
) from err
return result
else:
# Concat uses the first index to determine the final indexing order.
# The union of a shorter first index with the other indices causes
# the index sorting to be different from the order of the aggregating
# functions. Reindex if this is the case.
index_size = concatenated.index.size
full_ordered_index = next(result.index for result in results
if result.index.size == index_size)
return concatenated.reindex(full_ordered_index, copy=False)
