from __future__ import annotations import abc from collections import defaultdict from contextlib import nullcontext from functools import partial import inspect from typing import ( TYPE_CHECKING, Any, Callable, ContextManager, DefaultDict, Dict, Hashable, Iterable, Iterator, List, Sequence, cast, ) import numpy as np from pandas._config import option_context from pandas._libs import lib from pandas._typing import ( AggFuncType, AggFuncTypeBase, AggFuncTypeDict, AggObjType, Axis, AxisInt, NDFrameT, npt, ) from pandas.errors import SpecificationError from pandas.util._decorators import cache_readonly from pandas.core.dtypes.cast import is_nested_object from pandas.core.dtypes.common import ( is_dict_like, is_extension_array_dtype, is_list_like, is_sequence, ) from pandas.core.dtypes.generic import ( ABCDataFrame, ABCNDFrame, ABCSeries, ) from pandas.core.algorithms import safe_sort from pandas.core.base import SelectionMixin import pandas.core.common as com from pandas.core.construction import ensure_wrapped_if_datetimelike if TYPE_CHECKING: from pandas import ( DataFrame, Index, Series, ) from pandas.core.groupby import GroupBy from pandas.core.resample import Resampler from pandas.core.window.rolling import BaseWindow ResType = Dict[int, Any] def frame_apply( obj: DataFrame, func: AggFuncType, axis: Axis = 0, raw: bool = False, result_type: str | None = None, args=None, kwargs=None, ) -> FrameApply: """construct and return a row or column based frame apply object""" axis = obj._get_axis_number(axis) klass: type[FrameApply] if axis == 0: klass = FrameRowApply elif axis == 1: klass = FrameColumnApply return klass( obj, func, raw=raw, result_type=result_type, args=args, kwargs=kwargs, ) class Apply(metaclass=abc.ABCMeta): axis: AxisInt def __init__( self, obj: AggObjType, func, raw: bool, result_type: str | None, args, kwargs, ) -> None: self.obj = obj self.raw = raw self.args = args or () self.kwargs = kwargs or {} if result_type not in [None, "reduce", "broadcast", "expand"]: raise ValueError( "invalid value for result_type, must be one " "of {None, 'reduce', 'broadcast', 'expand'}" ) self.result_type = result_type # curry if needed if ( (kwargs or args) and not isinstance(func, (np.ufunc, str)) and not is_list_like(func) ): def f(x): return func(x, *args, **kwargs) else: f = func self.orig_f: AggFuncType = func self.f: AggFuncType = f @abc.abstractmethod def apply(self) -> DataFrame | Series: pass def agg(self) -> DataFrame | Series | None: """ Provide an implementation for the aggregators. Returns ------- Result of aggregation, or None if agg cannot be performed by this method. """ obj = self.obj arg = self.f args = self.args kwargs = self.kwargs if isinstance(arg, str): return self.apply_str() if is_dict_like(arg): return self.agg_dict_like() elif is_list_like(arg): # we require a list, but not a 'str' return self.agg_list_like() if callable(arg): f = com.get_cython_func(arg) if f and not args and not kwargs: return getattr(obj, f)() # caller can react return None def transform(self) -> DataFrame | Series: """ Transform a DataFrame or Series. Returns ------- DataFrame or Series Result of applying ``func`` along the given axis of the Series or DataFrame. Raises ------ ValueError If the transform function fails or does not transform. """ obj = self.obj func = self.orig_f axis = self.axis args = self.args kwargs = self.kwargs is_series = obj.ndim == 1 if obj._get_axis_number(axis) == 1: assert not is_series return obj.T.transform(func, 0, *args, **kwargs).T if is_list_like(func) and not is_dict_like(func): func = cast(List[AggFuncTypeBase], func) # Convert func equivalent dict if is_series: func = {com.get_callable_name(v) or v: v for v in func} else: func = {col: func for col in obj} if is_dict_like(func): func = cast(AggFuncTypeDict, func) return self.transform_dict_like(func) # func is either str or callable func = cast(AggFuncTypeBase, func) try: result = self.transform_str_or_callable(func) except TypeError: raise except Exception as err: raise ValueError("Transform function failed") from err # Functions that transform may return empty Series/DataFrame # when the dtype is not appropriate if ( isinstance(result, (ABCSeries, ABCDataFrame)) and result.empty and not obj.empty ): raise ValueError("Transform function failed") # error: Argument 1 to "__get__" of "AxisProperty" has incompatible type # "Union[Series, DataFrame, GroupBy[Any], SeriesGroupBy, # DataFrameGroupBy, BaseWindow, Resampler]"; expected "Union[DataFrame, # Series]" if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals( obj.index # type:ignore[arg-type] ): raise ValueError("Function did not transform") return result def transform_dict_like(self, func): """ Compute transform in the case of a dict-like func """ from pandas.core.reshape.concat import concat obj = self.obj args = self.args kwargs = self.kwargs # transform is currently only for Series/DataFrame assert isinstance(obj, ABCNDFrame) if len(func) == 0: raise ValueError("No transform functions were provided") func = self.normalize_dictlike_arg("transform", obj, func) results: dict[Hashable, DataFrame | Series] = {} for name, how in func.items(): colg = obj._gotitem(name, ndim=1) results[name] = colg.transform(how, 0, *args, **kwargs) return concat(results, axis=1) def transform_str_or_callable(self, func) -> DataFrame | Series: """ Compute transform in the case of a string or callable func """ obj = self.obj args = self.args kwargs = self.kwargs if isinstance(func, str): return self._try_aggregate_string_function(obj, func, *args, **kwargs) if not args and not kwargs: f = com.get_cython_func(func) if f: return getattr(obj, f)() # Two possible ways to use a UDF - apply or call directly try: return obj.apply(func, args=args, **kwargs) except Exception: return func(obj, *args, **kwargs) def agg_list_like(self) -> DataFrame | Series: """ Compute aggregation in the case of a list-like argument. Returns ------- Result of aggregation. """ from pandas.core.groupby.generic import ( DataFrameGroupBy, SeriesGroupBy, ) from pandas.core.reshape.concat import concat obj = self.obj arg = cast(List[AggFuncTypeBase], self.f) if getattr(obj, "axis", 0) == 1: raise NotImplementedError("axis other than 0 is not supported") 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 = [] is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy)) context_manager: ContextManager if is_groupby: # When as_index=False, we combine all results using indices # and adjust index after context_manager = com.temp_setattr(obj, "as_index", True) else: context_manager = nullcontext() with context_manager: # degenerate case if selected_obj.ndim == 1: for a in arg: colg = obj._gotitem(selected_obj.name, ndim=1, subset=selected_obj) if isinstance(colg, (ABCSeries, ABCDataFrame)): new_res = colg.aggregate( a, self.axis, *self.args, **self.kwargs ) else: new_res = colg.aggregate(a, *self.args, **self.kwargs) results.append(new_res) # make sure we find a good name name = com.get_callable_name(a) or a keys.append(name) else: indices = [] for index, col in enumerate(selected_obj): colg = obj._gotitem(col, ndim=1, subset=selected_obj.iloc[:, index]) if isinstance(colg, (ABCSeries, ABCDataFrame)): new_res = colg.aggregate( arg, self.axis, *self.args, **self.kwargs ) else: new_res = colg.aggregate(arg, *self.args, **self.kwargs) results.append(new_res) indices.append(index) keys = selected_obj.columns.take(indices) 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_dict_like(self) -> DataFrame | Series: """ Compute aggregation in the case of a dict-like argument. Returns ------- Result of aggregation. """ from pandas import Index from pandas.core.groupby.generic import ( DataFrameGroupBy, SeriesGroupBy, ) from pandas.core.reshape.concat import concat obj = self.obj arg = cast(AggFuncTypeDict, self.f) if getattr(obj, "axis", 0) == 1: raise NotImplementedError("axis other than 0 is not supported") if not isinstance(obj, SelectionMixin): # i.e. obj is Series or DataFrame selected_obj = obj selection = None else: selected_obj = obj._selected_obj selection = obj._selection arg = self.normalize_dictlike_arg("agg", selected_obj, arg) is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy)) context_manager: ContextManager if is_groupby: # When as_index=False, we combine all results using indices # and adjust index after context_manager = com.temp_setattr(obj, "as_index", True) else: context_manager = nullcontext() with context_manager: if selected_obj.ndim == 1: # key only used for output colg = obj._gotitem(selection, ndim=1) results = {key: colg.agg(how) for key, how in arg.items()} else: # key used for column selection and output results = { key: obj._gotitem(key, ndim=1).agg(how) for key, how in arg.items() } # set the final keys keys = list(arg.keys()) # Avoid making two isinstance calls in all and any below is_ndframe = [isinstance(r, ABCNDFrame) for r in results.values()] # combine results if all(is_ndframe): keys_to_use: Iterable[Hashable] keys_to_use = [k for k in keys if not results[k].empty] # Have to check, if at least one DataFrame is not empty. keys_to_use = keys_to_use if keys_to_use != [] else keys if selected_obj.ndim == 2: # keys are columns, so we can preserve names ktu = Index(keys_to_use) ktu._set_names(selected_obj.columns.names) keys_to_use = ktu axis: AxisInt = 0 if isinstance(obj, ABCSeries) else 1 result = concat( {k: results[k] for k in keys_to_use}, axis=axis, keys=keys_to_use, ) elif any(is_ndframe): # There is a mix of NDFrames and scalars raise ValueError( "cannot perform both aggregation " "and transformation operations " "simultaneously" ) else: from pandas import Series # we have a dict of scalars # GH 36212 use name only if obj is a series if obj.ndim == 1: obj = cast("Series", obj) name = obj.name else: name = None result = Series(results, name=name) return result def apply_str(self) -> DataFrame | Series: """ Compute apply in case of a string. Returns ------- result: Series or DataFrame """ # Caller is responsible for checking isinstance(self.f, str) f = cast(str, self.f) obj = self.obj # Support for `frame.transform('method')` # Some methods (shift, etc.) require the axis argument, others # don't, so inspect and insert if necessary. func = getattr(obj, f, None) if callable(func): sig = inspect.getfullargspec(func) arg_names = (*sig.args, *sig.kwonlyargs) if self.axis != 0 and ( "axis" not in arg_names or f in ("corrwith", "skew") ): raise ValueError(f"Operation {f} does not support axis=1") if "axis" in arg_names: self.kwargs["axis"] = self.axis return self._try_aggregate_string_function(obj, f, *self.args, **self.kwargs) def apply_multiple(self) -> DataFrame | Series: """ Compute apply in case of a list-like or dict-like. Returns ------- result: Series, DataFrame, or None Result when self.f is a list-like or dict-like, None otherwise. """ return self.obj.aggregate(self.f, self.axis, *self.args, **self.kwargs) def normalize_dictlike_arg( self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict ) -> AggFuncTypeDict: """ Handler for dict-like argument. Ensures that necessary columns exist if obj is a DataFrame, and that a nested renamer is not passed. Also normalizes to all lists when values consists of a mix of list and non-lists. """ assert how in ("apply", "agg", "transform") # Can't use func.values(); wouldn't work for a Series if ( how == "agg" and isinstance(obj, ABCSeries) and any(is_list_like(v) for _, v in func.items()) ) or (any(is_dict_like(v) for _, v in func.items())): # GH 15931 - deprecation of renaming keys raise SpecificationError("nested renamer is not supported") if obj.ndim != 1: # Check for missing columns on a frame cols = set(func.keys()) - set(obj.columns) if len(cols) > 0: cols_sorted = list(safe_sort(list(cols))) raise KeyError(f"Column(s) {cols_sorted} do not exist") aggregator_types = (list, tuple, dict) # if we have a dict of any non-scalars # eg. {'A' : ['mean']}, normalize all to # be list-likes # Cannot use func.values() because arg may be a Series if any(isinstance(x, aggregator_types) for _, x in func.items()): new_func: AggFuncTypeDict = {} for k, v in func.items(): if not isinstance(v, aggregator_types): new_func[k] = [v] else: new_func[k] = v func = new_func return func def _try_aggregate_string_function(self, obj, arg: str, *args, **kwargs): """ if arg is a string, then try to operate on it: - try to find a function (or attribute) on ourselves - try to find a numpy function - raise """ assert isinstance(arg, str) f = getattr(obj, arg, None) if f is not None: if callable(f): return f(*args, **kwargs) # people may try to aggregate on a non-callable attribute # but don't let them think they can pass args to it assert len(args) == 0 assert len([kwarg for kwarg in kwargs if kwarg not in ["axis"]]) == 0 return f f = getattr(np, arg, None) if f is not None and hasattr(obj, "__array__"): # in particular exclude Window return f(obj, *args, **kwargs) raise AttributeError( f"'{arg}' is not a valid function for '{type(obj).__name__}' object" ) class NDFrameApply(Apply): """ Methods shared by FrameApply and SeriesApply but not GroupByApply or ResamplerWindowApply """ obj: DataFrame | Series @property def index(self) -> Index: return self.obj.index @property def agg_axis(self) -> Index: return self.obj._get_agg_axis(self.axis) class FrameApply(NDFrameApply): obj: DataFrame # --------------------------------------------------------------- # Abstract Methods @property @abc.abstractmethod def result_index(self) -> Index: pass @property @abc.abstractmethod def result_columns(self) -> Index: pass @property @abc.abstractmethod def series_generator(self) -> Iterator[Series]: pass @abc.abstractmethod def wrap_results_for_axis( self, results: ResType, res_index: Index ) -> DataFrame | Series: pass # --------------------------------------------------------------- @property def res_columns(self) -> Index: return self.result_columns @property def columns(self) -> Index: return self.obj.columns @cache_readonly def values(self): return self.obj.values @cache_readonly def dtypes(self) -> Series: return self.obj.dtypes def apply(self) -> DataFrame | Series: """compute the results""" # dispatch to agg if is_list_like(self.f): return self.apply_multiple() # all empty if len(self.columns) == 0 and len(self.index) == 0: return self.apply_empty_result() # string dispatch if isinstance(self.f, str): return self.apply_str() # ufunc elif isinstance(self.f, np.ufunc): with np.errstate(all="ignore"): results = self.obj._mgr.apply("apply", func=self.f) # _constructor will retain self.index and self.columns return self.obj._constructor(data=results) # broadcasting if self.result_type == "broadcast": return self.apply_broadcast(self.obj) # one axis empty elif not all(self.obj.shape): return self.apply_empty_result() # raw elif self.raw: return self.apply_raw() return self.apply_standard() def agg(self): obj = self.obj axis = self.axis # TODO: Avoid having to change state self.obj = self.obj if self.axis == 0 else self.obj.T self.axis = 0 result = None try: result = super().agg() finally: self.obj = obj self.axis = axis if axis == 1: result = result.T if result is not None else result if result is None: result = self.obj.apply(self.orig_f, axis, args=self.args, **self.kwargs) return result def apply_empty_result(self): """ we have an empty result; at least 1 axis is 0 we will try to apply the function to an empty series in order to see if this is a reduction function """ assert callable(self.f) # we are not asked to reduce or infer reduction # so just return a copy of the existing object if self.result_type not in ["reduce", None]: return self.obj.copy() # we may need to infer should_reduce = self.result_type == "reduce" from pandas import Series if not should_reduce: try: if self.axis == 0: r = self.f(Series([], dtype=np.float64)) else: r = self.f(Series(index=self.columns, dtype=np.float64)) except Exception: pass else: should_reduce = not isinstance(r, Series) if should_reduce: if len(self.agg_axis): r = self.f(Series([], dtype=np.float64)) else: r = np.nan return self.obj._constructor_sliced(r, index=self.agg_axis) else: return self.obj.copy() def apply_raw(self): """apply to the values as a numpy array""" def wrap_function(func): """ Wrap user supplied function to work around numpy issue. see https://github.com/numpy/numpy/issues/8352 """ def wrapper(*args, **kwargs): result = func(*args, **kwargs) if isinstance(result, str): result = np.array(result, dtype=object) return result return wrapper result = np.apply_along_axis(wrap_function(self.f), self.axis, self.values) # TODO: mixed type case if result.ndim == 2: return self.obj._constructor(result, index=self.index, columns=self.columns) else: return self.obj._constructor_sliced(result, index=self.agg_axis) def apply_broadcast(self, target: DataFrame) -> DataFrame: assert callable(self.f) result_values = np.empty_like(target.values) # axis which we want to compare compliance result_compare = target.shape[0] for i, col in enumerate(target.columns): res = self.f(target[col]) ares = np.asarray(res).ndim # must be a scalar or 1d if ares > 1: raise ValueError("too many dims to broadcast") if ares == 1: # must match return dim if result_compare != len(res): raise ValueError("cannot broadcast result") result_values[:, i] = res # we *always* preserve the original index / columns result = self.obj._constructor( result_values, index=target.index, columns=target.columns ) return result def apply_standard(self): results, res_index = self.apply_series_generator() # wrap results return self.wrap_results(results, res_index) def apply_series_generator(self) -> tuple[ResType, Index]: assert callable(self.f) series_gen = self.series_generator res_index = self.result_index results = {} with option_context("mode.chained_assignment", None): for i, v in enumerate(series_gen): # ignore SettingWithCopy here in case the user mutates results[i] = self.f(v) if isinstance(results[i], ABCSeries): # If we have a view on v, we need to make a copy because # series_generator will swap out the underlying data results[i] = results[i].copy(deep=False) return results, res_index def wrap_results(self, results: ResType, res_index: Index) -> DataFrame | Series: from pandas import Series # see if we can infer the results if len(results) > 0 and 0 in results and is_sequence(results[0]): return self.wrap_results_for_axis(results, res_index) # dict of scalars # the default dtype of an empty Series is `object`, but this # code can be hit by df.mean() where the result should have dtype # float64 even if it's an empty Series. constructor_sliced = self.obj._constructor_sliced if len(results) == 0 and constructor_sliced is Series: result = constructor_sliced(results, dtype=np.float64) else: result = constructor_sliced(results) result.index = res_index return result def apply_str(self) -> DataFrame | Series: # Caller is responsible for checking isinstance(self.f, str) # TODO: GH#39993 - Avoid special-casing by replacing with lambda if self.f == "size": # Special-cased because DataFrame.size returns a single scalar obj = self.obj value = obj.shape[self.axis] return obj._constructor_sliced(value, index=self.agg_axis) return super().apply_str() class FrameRowApply(FrameApply): axis: AxisInt = 0 @property def series_generator(self): return (self.obj._ixs(i, axis=1) for i in range(len(self.columns))) @property def result_index(self) -> Index: return self.columns @property def result_columns(self) -> Index: return self.index def wrap_results_for_axis( self, results: ResType, res_index: Index ) -> DataFrame | Series: """return the results for the rows""" if self.result_type == "reduce": # e.g. test_apply_dict GH#8735 res = self.obj._constructor_sliced(results) res.index = res_index return res elif self.result_type is None and all( isinstance(x, dict) for x in results.values() ): # Our operation was a to_dict op e.g. # test_apply_dict GH#8735, test_apply_reduce_to_dict GH#25196 #37544 res = self.obj._constructor_sliced(results) res.index = res_index return res try: result = self.obj._constructor(data=results) except ValueError as err: if "All arrays must be of the same length" in str(err): # e.g. result = [[2, 3], [1.5], ['foo', 'bar']] # see test_agg_listlike_result GH#29587 res = self.obj._constructor_sliced(results) res.index = res_index return res else: raise if not isinstance(results[0], ABCSeries): if len(result.index) == len(self.res_columns): result.index = self.res_columns if len(result.columns) == len(res_index): result.columns = res_index return result class FrameColumnApply(FrameApply): axis: AxisInt = 1 def apply_broadcast(self, target: DataFrame) -> DataFrame: result = super().apply_broadcast(target.T) return result.T @property def series_generator(self): values = self.values values = ensure_wrapped_if_datetimelike(values) assert len(values) > 0 # We create one Series object, and will swap out the data inside # of it. Kids: don't do this at home. ser = self.obj._ixs(0, axis=0) mgr = ser._mgr if is_extension_array_dtype(ser.dtype): # values will be incorrect for this block # TODO(EA2D): special case would be unnecessary with 2D EAs obj = self.obj for i in range(len(obj)): yield obj._ixs(i, axis=0) else: for arr, name in zip(values, self.index): # GH#35462 re-pin mgr in case setitem changed it ser._mgr = mgr mgr.set_values(arr) object.__setattr__(ser, "_name", name) yield ser @property def result_index(self) -> Index: return self.index @property def result_columns(self) -> Index: return self.columns def wrap_results_for_axis( self, results: ResType, res_index: Index ) -> DataFrame | Series: """return the results for the columns""" result: DataFrame | Series # we have requested to expand if self.result_type == "expand": result = self.infer_to_same_shape(results, res_index) # we have a non-series and don't want inference elif not isinstance(results[0], ABCSeries): result = self.obj._constructor_sliced(results) result.index = res_index # we may want to infer results else: result = self.infer_to_same_shape(results, res_index) return result def infer_to_same_shape(self, results: ResType, res_index: Index) -> DataFrame: """infer the results to the same shape as the input object""" result = self.obj._constructor(data=results) result = result.T # set the index result.index = res_index # infer dtypes result = result.infer_objects(copy=False) return result class SeriesApply(NDFrameApply): obj: Series axis: AxisInt = 0 def __init__( self, obj: Series, func: AggFuncType, convert_dtype: bool, args, kwargs, ) -> None: self.convert_dtype = convert_dtype super().__init__( obj, func, raw=False, result_type=None, args=args, kwargs=kwargs, ) def apply(self) -> DataFrame | Series: obj = self.obj if len(obj) == 0: return self.apply_empty_result() # dispatch to agg if is_list_like(self.f): return self.apply_multiple() if isinstance(self.f, str): # if we are a string, try to dispatch return self.apply_str() # self.f is Callable return self.apply_standard() def agg(self): result = super().agg() if result is None: f = self.f kwargs = self.kwargs # string, list-like, and dict-like are entirely handled in super assert callable(f) # we can be called from an inner function which # passes this meta-data kwargs.pop("_level", None) # try a regular apply, this evaluates lambdas # row-by-row; however if the lambda is expected a Series # expression, e.g.: lambda x: x-x.quantile(0.25) # this will fail, so we can try a vectorized evaluation # we cannot FIRST try the vectorized evaluation, because # then .agg and .apply would have different semantics if the # operation is actually defined on the Series, e.g. str try: result = self.obj.apply(f) except (ValueError, AttributeError, TypeError): result = f(self.obj) return result def apply_empty_result(self) -> Series: obj = self.obj return obj._constructor(dtype=obj.dtype, index=obj.index).__finalize__( obj, method="apply" ) def apply_standard(self) -> DataFrame | Series: # caller is responsible for ensuring that f is Callable f = cast(Callable, self.f) obj = self.obj with np.errstate(all="ignore"): if isinstance(f, np.ufunc): return f(obj) # row-wise access if is_extension_array_dtype(obj.dtype) and hasattr(obj._values, "map"): # GH#23179 some EAs do not have `map` mapped = obj._values.map(f) else: values = obj.astype(object)._values mapped = lib.map_infer( values, f, convert=self.convert_dtype, ) if len(mapped) and isinstance(mapped[0], ABCSeries): # GH#43986 Need to do list(mapped) in order to get treated as nested # See also GH#25959 regarding EA support return obj._constructor_expanddim(list(mapped), index=obj.index) else: return obj._constructor(mapped, index=obj.index).__finalize__( obj, method="apply" ) class GroupByApply(Apply): def __init__( self, obj: GroupBy[NDFrameT], func: AggFuncType, args, kwargs, ) -> None: kwargs = kwargs.copy() self.axis = obj.obj._get_axis_number(kwargs.get("axis", 0)) super().__init__( obj, func, raw=False, result_type=None, args=args, kwargs=kwargs, ) def apply(self): raise NotImplementedError def transform(self): raise NotImplementedError class ResamplerWindowApply(Apply): axis: AxisInt = 0 obj: Resampler | BaseWindow def __init__( self, obj: Resampler | BaseWindow, func: AggFuncType, args, kwargs, ) -> None: super().__init__( obj, func, raw=False, result_type=None, args=args, kwargs=kwargs, ) def apply(self): raise NotImplementedError def transform(self): raise NotImplementedError def reconstruct_func( func: AggFuncType | None, **kwargs ) -> tuple[bool, AggFuncType | None, list[str] | None, npt.NDArray[np.intp] | None]: """ This is the internal function to reconstruct func given if there is relabeling or not and also normalize the keyword to get new order of columns. If named aggregation is applied, `func` will be None, and kwargs contains the column and aggregation function information to be parsed; If named aggregation is not applied, `func` is either string (e.g. 'min') or Callable, or list of them (e.g. ['min', np.max]), or the dictionary of column name and str/Callable/list of them (e.g. {'A': 'min'}, or {'A': [np.min, lambda x: x]}) If relabeling is True, will return relabeling, reconstructed func, column names, and the reconstructed order of columns. If relabeling is False, the columns and order will be None. Parameters ---------- func: agg function (e.g. 'min' or Callable) or list of agg functions (e.g. ['min', np.max]) or dictionary (e.g. {'A': ['min', np.max]}). **kwargs: dict, kwargs used in is_multi_agg_with_relabel and normalize_keyword_aggregation function for relabelling Returns ------- relabelling: bool, if there is relabelling or not func: normalized and mangled func columns: list of column names order: array of columns indices Examples -------- >>> reconstruct_func(None, **{"foo": ("col", "min")}) (True, defaultdict(, {'col': ['min']}), ('foo',), array([0])) >>> reconstruct_func("min") (False, 'min', None, None) """ relabeling = func is None and is_multi_agg_with_relabel(**kwargs) columns: list[str] | None = None order: npt.NDArray[np.intp] | None = None if not relabeling: if isinstance(func, list) and len(func) > len(set(func)): # GH 28426 will raise error if duplicated function names are used and # there is no reassigned name raise SpecificationError( "Function names must be unique if there is no new column names " "assigned" ) if func is None: # nicer error message raise TypeError("Must provide 'func' or tuples of '(column, aggfunc).") if relabeling: func, columns, order = normalize_keyword_aggregation(kwargs) return relabeling, func, columns, order def is_multi_agg_with_relabel(**kwargs) -> bool: """ Check whether kwargs passed to .agg look like multi-agg with relabeling. Parameters ---------- **kwargs : dict Returns ------- bool Examples -------- >>> is_multi_agg_with_relabel(a="max") False >>> is_multi_agg_with_relabel(a_max=("a", "max"), a_min=("a", "min")) True >>> is_multi_agg_with_relabel() False """ return all(isinstance(v, tuple) and len(v) == 2 for v in kwargs.values()) and ( len(kwargs) > 0 ) def normalize_keyword_aggregation( kwargs: dict, ) -> tuple[dict, list[str], npt.NDArray[np.intp]]: """ Normalize user-provided "named aggregation" kwargs. Transforms from the new ``Mapping[str, NamedAgg]`` style kwargs to the old Dict[str, List[scalar]]]. Parameters ---------- kwargs : dict Returns ------- aggspec : dict The transformed kwargs. columns : List[str] The user-provided keys. col_idx_order : List[int] List of columns indices. Examples -------- >>> normalize_keyword_aggregation({"output": ("input", "sum")}) (defaultdict(, {'input': ['sum']}), ('output',), array([0])) """ from pandas.core.indexes.base import Index # Normalize the aggregation functions as Mapping[column, List[func]], # process normally, then fixup the names. # TODO: aggspec type: typing.Dict[str, List[AggScalar]] # May be hitting https://github.com/python/mypy/issues/5958 # saying it doesn't have an attribute __name__ aggspec: DefaultDict = defaultdict(list) order = [] columns, pairs = list(zip(*kwargs.items())) for column, aggfunc in pairs: aggspec[column].append(aggfunc) order.append((column, com.get_callable_name(aggfunc) or aggfunc)) # uniquify aggfunc name if duplicated in order list uniquified_order = _make_unique_kwarg_list(order) # GH 25719, due to aggspec will change the order of assigned columns in aggregation # uniquified_aggspec will store uniquified order list and will compare it with order # based on index aggspec_order = [ (column, com.get_callable_name(aggfunc) or aggfunc) for column, aggfuncs in aggspec.items() for aggfunc in aggfuncs ] uniquified_aggspec = _make_unique_kwarg_list(aggspec_order) # get the new index of columns by comparison col_idx_order = Index(uniquified_aggspec).get_indexer(uniquified_order) return aggspec, columns, col_idx_order def _make_unique_kwarg_list( seq: Sequence[tuple[Any, Any]] ) -> Sequence[tuple[Any, Any]]: """ Uniquify aggfunc name of the pairs in the order list Examples: -------- >>> kwarg_list = [('a', ''), ('a', ''), ('b', '')] >>> _make_unique_kwarg_list(kwarg_list) [('a', '_0'), ('a', '_1'), ('b', '')] """ return [ (pair[0], f"{pair[1]}_{seq[:i].count(pair)}") if seq.count(pair) > 1 else pair for i, pair in enumerate(seq) ] def relabel_result( result: DataFrame | Series, func: dict[str, list[Callable | str]], columns: Iterable[Hashable], order: Iterable[int], ) -> dict[Hashable, Series]: """ Internal function to reorder result if relabelling is True for dataframe.agg, and return the reordered result in dict. Parameters: ---------- result: Result from aggregation func: Dict of (column name, funcs) columns: New columns name for relabelling order: New order for relabelling Examples: --------- >>> result = DataFrame({"A": [np.nan, 2, np.nan], ... "C": [6, np.nan, np.nan], "B": [np.nan, 4, 2.5]}) # doctest: +SKIP >>> funcs = {"A": ["max"], "C": ["max"], "B": ["mean", "min"]} >>> columns = ("foo", "aab", "bar", "dat") >>> order = [0, 1, 2, 3] >>> _relabel_result(result, func, columns, order) # doctest: +SKIP dict(A=Series([2.0, NaN, NaN, NaN], index=["foo", "aab", "bar", "dat"]), C=Series([NaN, 6.0, NaN, NaN], index=["foo", "aab", "bar", "dat"]), B=Series([NaN, NaN, 2.5, 4.0], index=["foo", "aab", "bar", "dat"])) """ from pandas.core.indexes.base import Index reordered_indexes = [ pair[0] for pair in sorted(zip(columns, order), key=lambda t: t[1]) ] reordered_result_in_dict: dict[Hashable, Series] = {} idx = 0 reorder_mask = not isinstance(result, ABCSeries) and len(result.columns) > 1 for col, fun in func.items(): s = result[col].dropna() # In the `_aggregate`, the callable names are obtained and used in `result`, and # these names are ordered alphabetically. e.g. # C2 C1 # 1 NaN # amax NaN 4.0 # max NaN 4.0 # sum 18.0 6.0 # Therefore, the order of functions for each column could be shuffled # accordingly so need to get the callable name if it is not parsed names, and # reorder the aggregated result for each column. # e.g. if df.agg(c1=("C2", sum), c2=("C2", lambda x: min(x))), correct order is # [sum, ], but in `result`, it will be [, sum], and we need to # reorder so that aggregated values map to their functions regarding the order. # However there is only one column being used for aggregation, not need to # reorder since the index is not sorted, and keep as is in `funcs`, e.g. # A # min 1.0 # mean 1.5 # mean 1.5 if reorder_mask: fun = [ com.get_callable_name(f) if not isinstance(f, str) else f for f in fun ] col_idx_order = Index(s.index).get_indexer(fun) s = s[col_idx_order] # assign the new user-provided "named aggregation" as index names, and reindex # it based on the whole user-provided names. s.index = reordered_indexes[idx : idx + len(fun)] reordered_result_in_dict[col] = s.reindex(columns, copy=False) idx = idx + len(fun) return reordered_result_in_dict # TODO: Can't use, because mypy doesn't like us setting __name__ # error: "partial[Any]" has no attribute "__name__" # the type is: # typing.Sequence[Callable[..., ScalarResult]] # -> typing.Sequence[Callable[..., ScalarResult]]: def _managle_lambda_list(aggfuncs: Sequence[Any]) -> Sequence[Any]: """ Possibly mangle a list of aggfuncs. Parameters ---------- aggfuncs : Sequence Returns ------- mangled: list-like A new AggSpec sequence, where lambdas have been converted to have unique names. Notes ----- If just one aggfunc is passed, the name will not be mangled. """ if len(aggfuncs) <= 1: # don't mangle for .agg([lambda x: .]) return aggfuncs i = 0 mangled_aggfuncs = [] for aggfunc in aggfuncs: if com.get_callable_name(aggfunc) == "": aggfunc = partial(aggfunc) aggfunc.__name__ = f"" i += 1 mangled_aggfuncs.append(aggfunc) return mangled_aggfuncs def maybe_mangle_lambdas(agg_spec: Any) -> Any: """ Make new lambdas with unique names. Parameters ---------- agg_spec : Any An argument to GroupBy.agg. Non-dict-like `agg_spec` are pass through as is. For dict-like `agg_spec` a new spec is returned with name-mangled lambdas. Returns ------- mangled : Any Same type as the input. Examples -------- >>> maybe_mangle_lambdas('sum') 'sum' >>> maybe_mangle_lambdas([lambda: 1, lambda: 2]) # doctest: +SKIP [, .f(*args, **kwargs)>] """ is_dict = is_dict_like(agg_spec) if not (is_dict or is_list_like(agg_spec)): return agg_spec mangled_aggspec = type(agg_spec)() # dict or OrderedDict if is_dict: for key, aggfuncs in agg_spec.items(): if is_list_like(aggfuncs) and not is_dict_like(aggfuncs): mangled_aggfuncs = _managle_lambda_list(aggfuncs) else: mangled_aggfuncs = aggfuncs mangled_aggspec[key] = mangled_aggfuncs else: mangled_aggspec = _managle_lambda_list(agg_spec) return mangled_aggspec def validate_func_kwargs( kwargs: dict, ) -> tuple[list[str], list[str | Callable[..., Any]]]: """ Validates types of user-provided "named aggregation" kwargs. `TypeError` is raised if aggfunc is not `str` or callable. Parameters ---------- kwargs : dict Returns ------- columns : List[str] List of user-provied keys. func : List[Union[str, callable[...,Any]]] List of user-provided aggfuncs Examples -------- >>> validate_func_kwargs({'one': 'min', 'two': 'max'}) (['one', 'two'], ['min', 'max']) """ tuple_given_message = "func is expected but received {} in **kwargs." columns = list(kwargs) func = [] for col_func in kwargs.values(): if not (isinstance(col_func, str) or callable(col_func)): raise TypeError(tuple_given_message.format(type(col_func).__name__)) func.append(col_func) if not columns: no_arg_message = "Must provide 'func' or named aggregation **kwargs." raise TypeError(no_arg_message) return columns, func