from __future__ import absolute_import, division, print_function import collections import itertools as it import operator import warnings import numpy as np import pandas as pd from .core import (DataFrame, Series, aca, map_partitions, merge, new_dd_object, no_default, split_out_on_index) from .methods import drop_columns from .shuffle import shuffle from .utils import make_meta, insert_meta_param_description, raise_on_meta_error from ..base import tokenize from ..utils import derived_from, M, funcname # ############################################# # # GroupBy implementation notes # # Dask groupby supports reductions, i.e., mean, sum and alike, and apply. The # former do not shuffle the data and are efficiently implemented as tree # reductions. The latter is implemented by shuffling the underlying partiitons # such that all items of a group can be found in the same parititon. # # The argument to ``.groupby``, the index, can be a ``str``, ``dd.DataFrame``, # ``dd.Series``, or a list thereof. In operations on the grouped object, the # divisions of the the grouped object and the items of index have to align. # Currently, there is no support to shuffle the index values as part of the # groupby operation. Therefore, the alignment has to be guaranteed by the # caller. # # To operate on matchings paritions, most groupby operations exploit the # corresponding support in ``apply_concat_apply``. Specifically, this function # operates on matching paritiotns of frame-like objects passed as varargs. # # After the inital chunk step, the passed index is implicitly passed along to # subsequent operations as the index of the parittions. Groupby operations on # the individual parttions can then access the index via the ``levels`` # parameter of the ``groupby`` function. The correct arguments is determined by # the ``_determine_levels`` function. # # To minimize overhead, series in an index that were obtained by getitem on the # object to group are not passed as series to the various operations, but as # columnn keys. This transformation is implemented as ``_normalize_index``. # # ############################################# def _determine_levels(index): """Determine the correct levels argument to groupby. """ if isinstance(index, (tuple, list)) and len(index) > 1: return list(range(len(index))) else: return 0 def _normalize_index(df, index): """Replace series with column names in an index wherever possible. """ if not isinstance(df, DataFrame): return index elif isinstance(index, list): return [_normalize_index(df, col) for col in index] elif (isinstance(index, Series) and index.name in df.columns and index._name == df[index.name]._name): return index.name elif (isinstance(index, DataFrame) and set(index.columns).issubset(df.columns) and index._name == df[index.columns]._name): return list(index.columns) else: return index def _maybe_slice(grouped, columns): """ Slice columns if grouped is pd.DataFrameGroupBy """ if isinstance(grouped, pd.core.groupby.DataFrameGroupBy): if columns is not None: if isinstance(columns, (tuple, list, set, pd.Index)): columns = list(columns) return grouped[columns] return grouped def _is_aligned(df, by): """Check if `df` and `by` have aligned indices""" if isinstance(by, (pd.Series, pd.DataFrame)): return df.index.equals(by.index) elif isinstance(by, (list, tuple)): return all(_is_aligned(df, i) for i in by) else: return True def _groupby_raise_unaligned(df, **kwargs): """Groupby, but raise if df and `by` key are unaligned. Pandas supports grouping by a column that doesn't align with the input frame/series/index. However, the reindexing this causes doesn't seem to be threadsafe, and can result in incorrect results. Since grouping by an unaligned key is generally a bad idea, we just error loudly in dask. For more information see pandas GH issue #15244 and Dask GH issue #1876.""" by = kwargs.get('by', None) if by is not None and not _is_aligned(df, by): msg = ("Grouping by an unaligned index is unsafe and unsupported.\n" "This can be caused by filtering only one of the object or\n" "grouping key. For example, the following works in pandas,\n" "but not in dask:\n" "\n" "df[df.foo < 0].groupby(df.bar)\n" "\n" "This can be avoided by either filtering beforehand, or\n" "passing in the name of the column instead:\n" "\n" "df2 = df[df.foo < 0]\n" "df2.groupby(df2.bar)\n" "# or\n" "df[df.foo < 0].groupby('bar')\n" "\n" "For more information see dask GH issue #1876.") raise ValueError(msg) return df.groupby(**kwargs) def _groupby_slice_apply(df, grouper, key, func): # No need to use raise if unaligned here - this is only called after # shuffling, which makes everything aligned already g = df.groupby(grouper) if key: g = g[key] return g.apply(func) def _groupby_get_group(df, by_key, get_key, columns): # SeriesGroupBy may pass df which includes group key grouped = _groupby_raise_unaligned(df, by=by_key) if get_key in grouped.groups: if isinstance(df, pd.DataFrame): grouped = grouped[columns] return grouped.get_group(get_key) else: # to create empty DataFrame/Series, which has the same # dtype as the original if isinstance(df, pd.DataFrame): # may be SeriesGroupBy df = df[columns] return df.iloc[0:0] ############################################################### # Aggregation ############################################################### def _groupby_aggregate(df, aggfunc=None, levels=None): return aggfunc(df.groupby(level=levels, sort=False)) def _apply_chunk(df, *index, **kwargs): func = kwargs.pop('chunk') columns = kwargs.pop('columns') g = _groupby_raise_unaligned(df, by=index) if isinstance(df, pd.Series) or columns is None: return func(g) else: if isinstance(columns, (tuple, list, set, pd.Index)): columns = list(columns) return func(g[columns]) def _var_chunk(df, *index): if isinstance(df, pd.Series): df = df.to_frame() g = _groupby_raise_unaligned(df, by=index) x = g.sum() x2 = g.agg(lambda x: (x**2).sum()).rename(columns=lambda c: c + '-x2') n = g.count().rename(columns=lambda c: c + '-count') return pd.concat([x, x2, n], axis=1) def _var_combine(g, levels): return g.groupby(level=levels, sort=False).sum() def _var_agg(g, levels, ddof): g = g.groupby(level=levels, sort=False).sum() nc = len(g.columns) x = g[g.columns[:nc // 3]] x2 = g[g.columns[nc // 3:2 * nc // 3]].rename(columns=lambda c: c[:-3]) n = g[g.columns[-nc // 3:]].rename(columns=lambda c: c[:-6]) # TODO: replace with _finalize_var? result = x2 - x ** 2 / n div = (n - ddof) div[div < 0] = 0 result /= div result[(n - ddof) == 0] = np.nan assert isinstance(result, pd.DataFrame) return result ############################################################### # nunique ############################################################### def _nunique_df_chunk(df, *index, **kwargs): levels = kwargs.pop('levels') name = kwargs.pop('name') g = _groupby_raise_unaligned(df, by=index) grouped = g[[name]].apply(pd.DataFrame.drop_duplicates) # we set the index here to force a possibly duplicate index # for our reduce step if isinstance(levels, list): grouped.index = pd.MultiIndex.from_arrays([ grouped.index.get_level_values(level=level) for level in levels ]) else: grouped.index = grouped.index.get_level_values(level=levels) return grouped def _nunique_df_combine(df, levels): result = df.groupby(level=levels, sort=False).apply(pd.DataFrame.drop_duplicates) if isinstance(levels, list): result.index = pd.MultiIndex.from_arrays([ result.index.get_level_values(level=level) for level in levels ]) else: result.index = result.index.get_level_values(level=levels) return result def _nunique_df_aggregate(df, levels, name): return df.groupby(level=levels, sort=False)[name].nunique() def _nunique_series_chunk(df, *index, **_ignored_): # convert series to data frame, then hand over to dataframe code path assert isinstance(df, pd.Series) df = df.to_frame() kwargs = dict(name=df.columns[0], levels=_determine_levels(index)) return _nunique_df_chunk(df, *index, **kwargs) ############################################################### # Aggregate support # # Aggregate is implemented as: # # 1. group-by-aggregate all partitions into intermediate values # 2. collect all partitions into a single partition # 3. group-by-aggregate the result into intermediate values # 4. transform all intermediate values into the result # # In Step 1 and 3 the dataframe is grouped on the same columns. # ############################################################### def _make_agg_id(func, column): return '{!s}-{!s}-{}'.format(func, column, tokenize(func, column)) def _normalize_spec(spec, non_group_columns): """ Return a list of ``(result_column, func, input_column)`` tuples. Spec can be - a function - a list of functions - a dictionary that maps input-columns to functions - a dictionary that maps input-columns to a lists of functions - a dictionary that maps input-columns to a dictionaries that map output-columns to functions. The non-group columns are a list of all column names that are not used in the groupby operation. Usually, the result columns are mutli-level names, returned as tuples. If only a single function is supplied or dictionary mapping columns to single functions, simple names are returned as strings (see the first two examples below). Examples -------- >>> _normalize_spec('mean', ['a', 'b', 'c']) [('a', 'mean', 'a'), ('b', 'mean', 'b'), ('c', 'mean', 'c')] >>> spec = collections.OrderedDict([('a', 'mean'), ('b', 'count')]) >>> _normalize_spec(spec, ['a', 'b', 'c']) [('a', 'mean', 'a'), ('b', 'count', 'b')] >>> _normalize_spec(['var', 'mean'], ['a', 'b', 'c']) ... # doctest: +NORMALIZE_WHITESPACE [(('a', 'var'), 'var', 'a'), (('a', 'mean'), 'mean', 'a'), \ (('b', 'var'), 'var', 'b'), (('b', 'mean'), 'mean', 'b'), \ (('c', 'var'), 'var', 'c'), (('c', 'mean'), 'mean', 'c')] >>> spec = collections.OrderedDict([('a', 'mean'), ('b', ['sum', 'count'])]) >>> _normalize_spec(spec, ['a', 'b', 'c']) ... # doctest: +NORMALIZE_WHITESPACE [(('a', 'mean'), 'mean', 'a'), (('b', 'sum'), 'sum', 'b'), \ (('b', 'count'), 'count', 'b')] >>> spec = collections.OrderedDict() >>> spec['a'] = ['mean', 'size'] >>> spec['b'] = collections.OrderedDict([('e', 'count'), ('f', 'var')]) >>> _normalize_spec(spec, ['a', 'b', 'c']) ... # doctest: +NORMALIZE_WHITESPACE [(('a', 'mean'), 'mean', 'a'), (('a', 'size'), 'size', 'a'), \ (('b', 'e'), 'count', 'b'), (('b', 'f'), 'var', 'b')] """ if not isinstance(spec, dict): spec = collections.OrderedDict(zip(non_group_columns, it.repeat(spec))) res = [] if isinstance(spec, dict): for input_column, subspec in spec.items(): if isinstance(subspec, dict): res.extend(((input_column, result_column), func, input_column) for result_column, func in subspec.items()) else: if not isinstance(subspec, list): subspec = [subspec] res.extend(((input_column, funcname(func)), func, input_column) for func in subspec) else: raise ValueError("unsupported agg spec of type {}".format(type(spec))) compounds = (list, tuple, dict) use_flat_columns = not any(isinstance(subspec, compounds) for subspec in spec.values()) if use_flat_columns: res = [(input_col, func, input_col) for (_, func, input_col) in res] return res def _build_agg_args(spec): """ Create transformation functions for a normalized aggregate spec. Parameters ---------- spec: a list of (result-column, aggregation-function, input-column) triples. To work with all arugment forms understood by pandas use ``_normalize_spec`` to normalize the argment before passing it on to ``_build_agg_args``. Returns ------- chunk_funcs: a list of (intermediate-column, function, keyword) triples that are applied on grouped chunks of the initial dataframe. agg_funcs: a list of (intermediate-column, functions, keword) triples that are applied on the grouped concatination of the preprocessed chunks. finalizers: a list of (result-column, function, keyword) triples that are applied after the ``agg_funcs``. They are used to create final results from intermediate representations. """ known_np_funcs = {np.min: 'min', np.max: 'max'} chunks = {} aggs = {} finalizers = [] for (result_column, func, input_column) in spec: func = funcname(known_np_funcs.get(func, func)) impls = _build_agg_args_single(result_column, func, input_column) # overwrite existing result-columns, generate intermedates only once chunks.update((spec[0], spec) for spec in impls['chunk_funcs']) aggs.update((spec[0], spec) for spec in impls['aggregate_funcs']) finalizers.append(impls['finalizer']) chunks = sorted(chunks.values()) aggs = sorted(aggs.values()) return chunks, aggs, finalizers def _build_agg_args_single(result_column, func, input_column): simple_impl = { 'sum': (M.sum, M.sum), 'min': (M.min, M.min), 'max': (M.max, M.max), 'count': (M.count, M.sum), 'size': (M.size, M.sum), } if func in simple_impl.keys(): return _build_agg_args_simple(result_column, func, input_column, simple_impl[func]) elif func == 'var': return _build_agg_args_var(result_column, func, input_column) elif func == 'std': return _build_agg_args_std(result_column, func, input_column) elif func == 'mean': return _build_agg_args_mean(result_column, func, input_column) else: raise ValueError("unknown aggregate {}".format(func)) def _build_agg_args_simple(result_column, func, input_column, impl_pair): intermediate = _make_agg_id(func, input_column) chunk_impl, agg_impl = impl_pair return dict( chunk_funcs=[(intermediate, _apply_func_to_column, dict(column=input_column, func=chunk_impl))], aggregate_funcs=[(intermediate, _apply_func_to_column, dict(column=intermediate, func=agg_impl))], finalizer=(result_column, operator.itemgetter(intermediate), dict()), ) def _build_agg_args_var(result_column, func, input_column): int_sum = _make_agg_id('sum', input_column) int_sum2 = _make_agg_id('sum2', input_column) int_count = _make_agg_id('count', input_column) return dict( chunk_funcs=[ (int_sum, _apply_func_to_column, dict(column=input_column, func=M.sum)), (int_count, _apply_func_to_column, dict(column=input_column, func=M.count)), (int_sum2, _compute_sum_of_squares, dict(column=input_column)), ], aggregate_funcs=[ (col, _apply_func_to_column, dict(column=col, func=M.sum)) for col in (int_sum, int_count, int_sum2) ], finalizer=(result_column, _finalize_var, dict(sum_column=int_sum, count_column=int_count, sum2_column=int_sum2)), ) def _build_agg_args_std(result_column, func, input_column): impls = _build_agg_args_var(result_column, func, input_column) result_column, _, kwargs = impls['finalizer'] impls['finalizer'] = (result_column, _finalize_std, kwargs) return impls def _build_agg_args_mean(result_column, func, input_column): int_sum = _make_agg_id('sum', input_column) int_count = _make_agg_id('count', input_column) return dict( chunk_funcs=[ (int_sum, _apply_func_to_column, dict(column=input_column, func=M.sum)), (int_count, _apply_func_to_column, dict(column=input_column, func=M.count)), ], aggregate_funcs=[ (col, _apply_func_to_column, dict(column=col, func=M.sum)) for col in (int_sum, int_count) ], finalizer=(result_column, _finalize_mean, dict(sum_column=int_sum, count_column=int_count)), ) def _groupby_apply_funcs(df, *index, **kwargs): """ Group a dataframe and apply multiple aggregation functions. Parameters ---------- df: pandas.DataFrame The dataframe to work on. index: list of groupers If given, they are added to the keyword arguments as the ``by`` argument. funcs: list of result-colum, function, keywordargument triples The list of functions that are applied on the grouped data frame. Has to be passed as a keyword argument. kwargs: All keyword arguments, but ``funcs``, are passed verbatim to the groupby operation of the dataframe Returns ------- aggregated: the aggregated dataframe. """ if len(index): kwargs.update(by=list(index)) funcs = kwargs.pop('funcs') grouped = _groupby_raise_unaligned(df, **kwargs) result = collections.OrderedDict() for result_column, func, func_kwargs in funcs: result[result_column] = func(grouped, **func_kwargs) return pd.DataFrame(result) def _compute_sum_of_squares(grouped, column): base = grouped[column] if column is not None else grouped return base.apply(lambda x: (x ** 2).sum()) def _agg_finalize(df, funcs): result = collections.OrderedDict() for result_column, func, kwargs in funcs: result[result_column] = func(df, **kwargs) return pd.DataFrame(result) def _apply_func_to_column(df_like, column, func): if column is None: return func(df_like) return func(df_like[column]) def _finalize_mean(df, sum_column, count_column): return df[sum_column] / df[count_column] def _finalize_var(df, count_column, sum_column, sum2_column, ddof=1): n = df[count_column] x = df[sum_column] x2 = df[sum2_column] result = x2 - x ** 2 / n div = (n - ddof) div[div < 0] = 0 result /= div result[(n - ddof) == 0] = np.nan return result def _finalize_std(df, count_column, sum_column, sum2_column, ddof=1): result = _finalize_var(df, count_column, sum_column, sum2_column, ddof) return np.sqrt(result) def _cum_agg_aligned(part, cum_last, index, columns, func, initial): align = cum_last.reindex(part.set_index(index).index, fill_value=initial) align.index = part.index return func(part[columns], align) def _cum_agg_filled(a, b, func, initial): union = a.index.union(b.index) return func(a.reindex(union, fill_value=initial), b.reindex(union, fill_value=initial), fill_value=initial) def _cumcount_aggregate(a, b, fill_value=None): return a.add(b, fill_value=fill_value) + 1 class _GroupBy(object): """ Superclass for DataFrameGroupBy and SeriesGroupBy Parameters ---------- obj: DataFrame or Series DataFrame or Series to be grouped by: str, list or Series The key for grouping slice: str, list The slice keys applied to GroupBy result """ def __init__(self, df, by=None, slice=None): assert isinstance(df, (DataFrame, Series)) self.obj = df # grouping key passed via groupby method self.index = _normalize_index(df, by) if isinstance(self.index, list): do_index_partition_align = all( item.divisions == df.divisions if isinstance(item, Series) else True for item in self.index ) elif isinstance(self.index, Series): do_index_partition_align = df.divisions == self.index.divisions else: do_index_partition_align = True if not do_index_partition_align: raise NotImplementedError("The grouped object and index of the " "groupby must have the same divisions.") # slicing key applied to _GroupBy instance self._slice = slice if isinstance(self.index, list): index_meta = [item._meta if isinstance(item, Series) else item for item in self.index] elif isinstance(self.index, Series): index_meta = self.index._meta else: index_meta = self.index self._meta = self.obj._meta.groupby(index_meta) @property def _meta_nonempty(self): """ Return a pd.DataFrameGroupBy / pd.SeriesGroupBy which contains sample data. """ sample = self.obj._meta_nonempty if isinstance(self.index, list): index_meta = [item._meta_nonempty if isinstance(item, Series) else item for item in self.index] elif isinstance(self.index, Series): index_meta = self.index._meta_nonempty else: index_meta = self.index grouped = sample.groupby(index_meta) return _maybe_slice(grouped, self._slice) def _aca_agg(self, token, func, aggfunc=None, split_every=None, split_out=1): if aggfunc is None: aggfunc = func meta = func(self._meta) columns = meta.name if isinstance(meta, pd.Series) else meta.columns token = self._token_prefix + token levels = _determine_levels(self.index) return aca([self.obj, self.index] if not isinstance(self.index, list) else [self.obj] + self.index, chunk=_apply_chunk, chunk_kwargs=dict(chunk=func, columns=columns), aggregate=_groupby_aggregate, meta=meta, token=token, split_every=split_every, aggregate_kwargs=dict(aggfunc=aggfunc, levels=levels), split_out=split_out, split_out_setup=split_out_on_index) def _cum_agg(self, token, chunk, aggregate, initial): """ Wrapper for cumulative groupby operation """ meta = chunk(self._meta) columns = meta.name if isinstance(meta, pd.Series) else meta.columns index = self.index if isinstance(self.index, list) else [self.index] name = self._token_prefix + token name_part = name + '-map' name_last = name + '-take-last' name_cum = name + '-cum-last' # cumulate each partitions cumpart_raw = map_partitions(_apply_chunk, self.obj, *index, chunk=chunk, columns=columns, token=name_part, meta=meta) cumpart_ext = (cumpart_raw.to_frame() if isinstance(meta, pd.Series) else cumpart_raw).assign(**{i: self.obj[i] for i in index}) cumlast = map_partitions(_apply_chunk, cumpart_ext, *index, columns=0 if columns is None else columns, chunk=M.last, meta=meta, token=name_last) # aggregate cumulated partisions and its previous last element dask = {} dask[(name, 0)] = (cumpart_raw._name, 0) for i in range(1, self.obj.npartitions): # store each cumulative step to graph to reduce computation if i == 1: dask[(name_cum, i)] = (cumlast._name, i - 1) else: # aggregate with previous cumulation results dask[(name_cum, i)] = (_cum_agg_filled, (name_cum, i - 1), (cumlast._name, i - 1), aggregate, initial) dask[(name, i)] = (_cum_agg_aligned, (cumpart_ext._name, i), (name_cum, i), index, 0 if columns is None else columns, aggregate, initial) return new_dd_object(merge(dask, cumpart_ext.dask, cumlast.dask), name, chunk(self._meta), self.obj.divisions) @derived_from(pd.core.groupby.GroupBy) def cumsum(self, axis=0): if axis: return self.obj.cumsum(axis=axis) else: return self._cum_agg('cumsum', chunk=M.cumsum, aggregate=M.add, initial=0) @derived_from(pd.core.groupby.GroupBy) def cumprod(self, axis=0): if axis: return self.obj.cumprod(axis=axis) else: return self._cum_agg('cumprod', chunk=M.cumprod, aggregate=M.mul, initial=1) @derived_from(pd.core.groupby.GroupBy) def cumcount(self, axis=None): return self._cum_agg('cumcount', chunk=M.cumcount, aggregate=_cumcount_aggregate, initial=-1) @derived_from(pd.core.groupby.GroupBy) def sum(self, split_every=None, split_out=1): return self._aca_agg(token='sum', func=M.sum, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.GroupBy) def min(self, split_every=None, split_out=1): return self._aca_agg(token='min', func=M.min, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.GroupBy) def max(self, split_every=None, split_out=1): return self._aca_agg(token='max', func=M.max, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.GroupBy) def count(self, split_every=None, split_out=1): return self._aca_agg(token='count', func=M.count, aggfunc=M.sum, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.GroupBy) def mean(self, split_every=None, split_out=1): return (self.sum(split_every=split_every, split_out=split_out) / self.count(split_every=split_every, split_out=split_out)) @derived_from(pd.core.groupby.GroupBy) def size(self, split_every=None, split_out=1): return self._aca_agg(token='size', func=M.size, aggfunc=M.sum, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.GroupBy) def var(self, ddof=1, split_every=None, split_out=1): levels = _determine_levels(self.index) result = aca([self.obj, self.index] if not isinstance(self.index, list) else [self.obj] + self.index, chunk=_var_chunk, aggregate=_var_agg, combine=_var_combine, token=self._token_prefix + 'var', aggregate_kwargs={'ddof': ddof, 'levels': levels}, combine_kwargs={'levels': levels}, split_every=split_every, split_out=split_out, split_out_setup=split_out_on_index) if isinstance(self.obj, Series): result = result[result.columns[0]] if self._slice: result = result[self._slice] return result @derived_from(pd.core.groupby.GroupBy) def std(self, ddof=1, split_every=None, split_out=1): v = self.var(ddof, split_every=split_every, split_out=split_out) result = map_partitions(np.sqrt, v, meta=v) return result @derived_from(pd.core.groupby.GroupBy) def get_group(self, key): token = self._token_prefix + 'get_group' meta = self._meta.obj if isinstance(meta, pd.DataFrame) and self._slice is not None: meta = meta[self._slice] columns = meta.columns if isinstance(meta, pd.DataFrame) else meta.name return map_partitions(_groupby_get_group, self.obj, self.index, key, columns, meta=meta, token=token) def aggregate(self, arg, split_every, split_out=1): if isinstance(self.obj, DataFrame): if isinstance(self.index, tuple) or np.isscalar(self.index): group_columns = {self.index} elif isinstance(self.index, list): group_columns = {i for i in self.index if isinstance(i, tuple) or np.isscalar(i)} else: group_columns = set() if self._slice: # pandas doesn't exclude the grouping column in a SeriesGroupBy # like df.groupby('a')['a'].agg(...) non_group_columns = self._slice if not isinstance(non_group_columns, list): non_group_columns = [non_group_columns] else: # NOTE: this step relies on the index normalization to replace # series with their name in an index. non_group_columns = [col for col in self.obj.columns if col not in group_columns] spec = _normalize_spec(arg, non_group_columns) elif isinstance(self.obj, Series): # implementation detail: if self.obj is a series, a pseudo column # None is used to denote the series itself. This pseudo column is # removed from the result columns before passing the spec along. spec = _normalize_spec({None: arg}, []) spec = [(result_column, func, input_column) for ((_, result_column), func, input_column) in spec] else: raise ValueError("aggregate on unknown object {}".format(self.obj)) chunk_funcs, aggregate_funcs, finalizers = _build_agg_args(spec) if isinstance(self.index, (tuple, list)) and len(self.index) > 1: levels = list(range(len(self.index))) else: levels = 0 if not isinstance(self.index, list): chunk_args = [self.obj, self.index] else: chunk_args = [self.obj] + self.index obj = aca(chunk_args, chunk=_groupby_apply_funcs, chunk_kwargs=dict(funcs=chunk_funcs), aggregate=_groupby_apply_funcs, aggregate_kwargs=dict(funcs=aggregate_funcs, level=levels), combine=_groupby_apply_funcs, combine_kwargs=dict(funcs=aggregate_funcs, level=levels), token='aggregate', split_every=split_every, split_out=split_out, split_out_setup=split_out_on_index) return map_partitions(_agg_finalize, obj, token='aggregate-finalize', funcs=finalizers) @insert_meta_param_description(pad=12) def apply(self, func, meta=no_default): """ Parallel version of pandas GroupBy.apply This mimics the pandas version except for the following: 1. The user should provide output metadata. 2. If the grouper does not align with the index then this causes a full shuffle. The order of rows within each group may not be preserved. Parameters ---------- func: function Function to apply $META Returns ------- applied : Series or DataFrame depending on columns keyword """ if meta is no_default: msg = ("`meta` is not specified, inferred from partial data. " "Please provide `meta` if the result is unexpected.\n" " Before: .apply(func)\n" " After: .apply(func, meta={'x': 'f8', 'y': 'f8'}) for dataframe result\n" " or: .apply(func, meta=('x', 'f8')) for series result") warnings.warn(msg, stacklevel=2) with raise_on_meta_error("groupby.apply({0})".format(funcname(func))): meta = self._meta_nonempty.apply(func) meta = make_meta(meta) df = self.obj if isinstance(self.index, DataFrame): # add index columns to dataframe df2 = df.assign(**{'_index_' + c: self.index[c] for c in self.index.columns}) index = self.index elif isinstance(self.index, Series): df2 = df.assign(_index=self.index) index = self.index elif (isinstance(self.index, list) and any(isinstance(item, Series) for item in self.index)): raise NotImplementedError("groupby-apply with a multiple Series " "is currently not supported") else: df2 = df index = df[self.index] df3 = shuffle(df2, index) # shuffle dataframe and index if isinstance(self.index, DataFrame): # extract index from dataframe cols = ['_index_' + c for c in self.index.columns] index2 = df3[cols] if isinstance(meta, pd.DataFrame): df4 = df3.map_partitions(drop_columns, cols, meta.columns.dtype) else: df4 = df3.drop(cols, axis=1) elif isinstance(self.index, Series): index2 = df3['_index'] index2.name = self.index.name if isinstance(meta, pd.DataFrame): df4 = df3.map_partitions(drop_columns, '_index', meta.columns.dtype) else: df4 = df3.drop('_index', axis=1) else: df4 = df3 index2 = self.index # Perform embarrassingly parallel groupby-apply df5 = map_partitions(_groupby_slice_apply, df4, index2, self._slice, func, meta=meta) return df5 class DataFrameGroupBy(_GroupBy): _token_prefix = 'dataframe-groupby-' def __getitem__(self, key): if isinstance(key, list): g = DataFrameGroupBy(self.obj, by=self.index, slice=key) else: g = SeriesGroupBy(self.obj, by=self.index, slice=key) # error is raised from pandas g._meta = g._meta[key] return g def __dir__(self): return sorted(set(dir(type(self)) + list(self.__dict__) + list(filter(pd.compat.isidentifier, self.obj.columns)))) def __getattr__(self, key): try: return self[key] except KeyError as e: raise AttributeError(e) @derived_from(pd.core.groupby.DataFrameGroupBy) def aggregate(self, arg, split_every=None, split_out=1): if arg == 'size': return self.size() return super(DataFrameGroupBy, self).aggregate(arg, split_every=split_every, split_out=split_out) @derived_from(pd.core.groupby.DataFrameGroupBy) def agg(self, arg, split_every=None, split_out=1): return self.aggregate(arg, split_every=split_every, split_out=split_out) class SeriesGroupBy(_GroupBy): _token_prefix = 'series-groupby-' def __init__(self, df, by=None, slice=None): # for any non series object, raise pandas-compat error message if isinstance(df, Series): if isinstance(by, Series): pass elif isinstance(by, list): if len(by) == 0: raise ValueError("No group keys passed!") non_series_items = [item for item in by if not isinstance(item, Series)] # raise error from pandas, if applicable df._meta.groupby(non_series_items) else: # raise error from pandas, if applicable df._meta.groupby(by) super(SeriesGroupBy, self).__init__(df, by=by, slice=slice) def nunique(self, split_every=None, split_out=1): name = self._meta.obj.name levels = _determine_levels(self.index) if isinstance(self.obj, DataFrame): chunk = _nunique_df_chunk else: chunk = _nunique_series_chunk return aca([self.obj, self.index] if not isinstance(self.index, list) else [self.obj] + self.index, chunk=chunk, aggregate=_nunique_df_aggregate, combine=_nunique_df_combine, token='series-groupby-nunique', chunk_kwargs={'levels': levels, 'name': name}, aggregate_kwargs={'levels': levels, 'name': name}, combine_kwargs={'levels': levels}, split_every=split_every, split_out=split_out, split_out_setup=split_out_on_index) @derived_from(pd.core.groupby.SeriesGroupBy) def aggregate(self, arg, split_every=None, split_out=1): # short-circuit 'simple' aggregations if ( not isinstance(arg, (list, dict)) and arg in {'sum', 'mean', 'var', 'size', 'std', 'count'} ): return getattr(self, arg)(split_every=split_every, split_out=split_out) result = super(SeriesGroupBy, self).aggregate(arg, split_every=split_every, split_out=split_out) if self._slice: result = result[self._slice] return result @derived_from(pd.core.groupby.SeriesGroupBy) def agg(self, arg, split_every=None, split_out=1): return self.aggregate(arg, split_every=split_every, split_out=split_out)