def _reconstruct_func(func, **kwargs):
relabeling_required, func, new_columns, order = reconstruct_func(
func, **kwargs)
# We convert to the string version of the function for simplicity.
func = {
k: v if not callable(v) or v.__name__ not in dir(self) else
v.__name__
for k, v in func.items()
}
return relabeling_required, func, new_columns, order
def aggregate(self, func=None, *args, **kwargs):
if self._axis != 0:
# This is not implemented in pandas,
# so we throw a different message
raise NotImplementedError("axis other than 0 is not supported")
if (
callable(func)
and isinstance(func, BuiltinFunctionType)
and func.__name__ in dir(self)
):
func = func.__name__
relabeling_required = False
if isinstance(func, dict) or func is None:
def try_get_str_func(fn):
if not isinstance(fn, str) and isinstance(fn, Iterable):
return [try_get_str_func(f) for f in fn]
return fn.__name__ if callable(fn) and fn.__name__ in dir(self) else fn
relabeling_required, func_dict, new_columns, order = reconstruct_func(
func, **kwargs
)
func_dict = {col: try_get_str_func(fn) for col, fn in func_dict.items()}
if any(i not in self._df.columns for i in func_dict.keys()):
from pandas.core.base import SpecificationError
raise SpecificationError("nested renamer is not supported")
if func is None:
kwargs = {}
func = func_dict
elif is_list_like(func):
return self._default_to_pandas(
lambda df, *args, **kwargs: df.aggregate(func, *args, **kwargs),
*args,
**kwargs,
)
elif callable(func):
return self._apply_agg_function(
lambda grp, *args, **kwargs: grp.aggregate(func, *args, **kwargs),
*args,
**kwargs,
)
elif isinstance(func, str):
# Using "getattr" here masks possible AttributeError which we throw
# in __getattr__, so we should call __getattr__ directly instead.
agg_func = self.__getattr__(func)
if callable(agg_func):
return agg_func(*args, **kwargs)
result = self._apply_agg_function(
func,
*args,
**kwargs,
)
if relabeling_required:
result = result.iloc[:, order]
result.columns = new_columns
return result
def aggregate(self, func=None, *args, **kwargs):
if self._axis != 0:
# This is not implemented in pandas,
# so we throw a different message
raise NotImplementedError("axis other than 0 is not supported")
if (
callable(func)
and isinstance(func, BuiltinFunctionType)
and func.__name__ in dir(self)
):
func = func.__name__
relabeling_required = False
if isinstance(func, dict) or func is None:
def try_get_str_func(fn):
if not isinstance(fn, str) and isinstance(fn, Iterable):
return [try_get_str_func(f) for f in fn]
return fn.__name__ if callable(fn) and fn.__name__ in dir(self) else fn
relabeling_required, func_dict, new_columns, order = reconstruct_func(
func, **kwargs
)
func_dict = {col: try_get_str_func(fn) for col, fn in func_dict.items()}
if any(i not in self._df.columns for i in func_dict.keys()):
from pandas.core.base import SpecificationError
raise SpecificationError("nested renamer is not supported")
if func is None:
kwargs = {}
func = func_dict
elif is_list_like(func):
return self._default_to_pandas(
lambda df, *args, **kwargs: df.aggregate(func, *args, **kwargs),
*args,
**kwargs,
)
elif callable(func):
return self._apply_agg_function(
lambda grp, *args, **kwargs: grp.aggregate(func, *args, **kwargs),
*args,
**kwargs,
)
elif isinstance(func, str):
# Using "getattr" here masks possible AttributeError which we throw
# in __getattr__, so we should call __getattr__ directly instead.
agg_func = self.__getattr__(func)
if callable(agg_func):
return agg_func(*args, **kwargs)
result = self._apply_agg_function(
func,
*args,
**kwargs,
)
if relabeling_required:
if not self._as_index:
nby_cols = len(result.columns) - len(new_columns)
order = np.concatenate([np.arange(nby_cols), order + nby_cols])
by_cols = result.columns[:nby_cols]
new_columns = pandas.Index(new_columns)
if by_cols.nlevels != new_columns.nlevels:
by_cols = by_cols.remove_unused_levels()
empty_levels = [
i
for i, level in enumerate(by_cols.levels)
if len(level) == 1 and level[0] == ""
]
by_cols = by_cols.droplevel(empty_levels)
new_columns = by_cols.append(new_columns)
result = result.iloc[:, order]
result.columns = new_columns
return result
def aggregate(self, func=None, *args, **kwargs):
if self._axis != 0:
# This is not implemented in pandas,
# so we throw a different message
raise NotImplementedError("axis other than 0 is not supported")
if (
callable(func)
and isinstance(func, BuiltinFunctionType)
and func.__name__ in dir(self)
):
func = func.__name__
relabeling_required = False
if isinstance(func, dict) or func is None:
def try_get_str_func(fn):
if not isinstance(fn, str) and isinstance(fn, Iterable):
return [try_get_str_func(f) for f in fn]
return fn.__name__ if callable(fn) and fn.__name__ in dir(self) else fn
relabeling_required, func_dict, new_columns, order = reconstruct_func(
func, **kwargs
)
func_dict = {col: try_get_str_func(fn) for col, fn in func_dict.items()}
if (
relabeling_required
and not self._as_index
and any(col in func_dict for col in self._internal_by)
):
ErrorMessage.missmatch_with_pandas(
operation="GroupBy.aggregate(**dictionary_renaming_aggregation)",
message=(
"intersection of the columns to aggregate and 'by' is not yet supported when 'as_index=False', "
"columns with group names of the intersection will not be presented in the result. "
"To achieve the desired result rewrite the original code from:\n"
"df.groupby('by_column', as_index=False).agg(agg_func=('by_column', agg_func))\n"
"to the:\n"
"df.groupby('by_column').agg(agg_func=('by_column', agg_func)).reset_index()"
),
)
if any(i not in self._df.columns for i in func_dict.keys()):
from pandas.core.base import SpecificationError
raise SpecificationError("nested renamer is not supported")
if func is None:
kwargs = {}
func = func_dict
elif is_list_like(func):
return self._default_to_pandas(
lambda df, *args, **kwargs: df.aggregate(func, *args, **kwargs),
*args,
**kwargs,
)
elif callable(func):
return self._check_index(
self._wrap_aggregation(
qc_method=type(self._query_compiler).groupby_agg,
numeric_only=False,
agg_func=func,
agg_args=args,
agg_kwargs=kwargs,
how="axis_wise",
)
)
elif isinstance(func, str):
# Using "getattr" here masks possible AttributeError which we throw
# in __getattr__, so we should call __getattr__ directly instead.
agg_func = self.__getattr__(func)
if callable(agg_func):
return agg_func(*args, **kwargs)
result = self._wrap_aggregation(
qc_method=type(self._query_compiler).groupby_agg,
numeric_only=False,
agg_func=func,
agg_args=args,
agg_kwargs=kwargs,
how="axis_wise",
)
if relabeling_required:
if not self._as_index:
nby_cols = len(result.columns) - len(new_columns)
order = np.concatenate([np.arange(nby_cols), order + nby_cols])
by_cols = result.columns[:nby_cols]
new_columns = pandas.Index(new_columns)
if by_cols.nlevels != new_columns.nlevels:
by_cols = by_cols.remove_unused_levels()
empty_levels = [
i
for i, level in enumerate(by_cols.levels)
if len(level) == 1 and level[0] == ""
]
by_cols = by_cols.droplevel(empty_levels)
new_columns = by_cols.append(new_columns)
result = result.iloc[:, order]
result.columns = new_columns
return result
