import collections
import itertools as it
import operator
import uuid
import warnings
from numbers import Integral
import numpy as np
import pandas as pd
from dask import config
from dask.base import tokenize
from dask.dataframe._compat import PANDAS_GT_150, check_numeric_only_deprecation
from dask.dataframe.core import (
GROUP_KEYS_DEFAULT,
DataFrame,
Series,
_extract_meta,
_Frame,
aca,
map_partitions,
new_dd_object,
no_default,
split_out_on_index,
)
from dask.dataframe.dispatch import grouper_dispatch
from dask.dataframe.methods import concat, drop_columns
from dask.dataframe.shuffle import shuffle
from dask.dataframe.utils import (
PANDAS_GT_110,
insert_meta_param_description,
is_dataframe_like,
is_series_like,
make_meta,
raise_on_meta_error,
)
from dask.highlevelgraph import HighLevelGraph
from dask.utils import M, _deprecated, derived_from, funcname, itemgetter
# #############################################
#
# 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 partitions
# such that all items of a group can be found in the same partition.
#
# The argument to ``.groupby`` (``by``), 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 ``by`` have to align.
# Currently, there is no support to shuffle the ``by`` values as part of the
# groupby operation. Therefore, the alignment has to be guaranteed by the
# caller.
#
# To operate on matching partitions, most groupby operations exploit the
# corresponding support in ``apply_concat_apply``. Specifically, this function
# operates on matching partitions of frame-like objects passed as varargs.
#
# After the initial chunk step, ``by``` is implicitly passed along to
# subsequent operations as the index of the partitions. Groupby operations on
# the individual partitions can then access ``by`` via the ``levels``
# parameter of the ``groupby`` function. The correct argument is determined by
# the ``_determine_levels`` function.
#
# To minimize overhead, any ``by`` that is a series contained within the
# dataframe is passed as a columnn key. This transformation is implemented as
# ``_normalize_by``.
#
# #############################################
SORT_SPLIT_OUT_WARNING = (
"In the future, `sort` for groupby operations will default to `True`"
" to match the behavior of pandas. However, `sort=True` does not work"
" with `split_out>1`. To retain the current behavior for multiple"
" output partitions, set `sort=False`."
)
def _determine_levels(by):
"""Determine the correct levels argument to groupby."""
if isinstance(by, (tuple, list)) and len(by) > 1:
return list(range(len(by)))
else:
return 0
def _normalize_by(df, by):
"""Replace series with column names wherever possible."""
if not isinstance(df, DataFrame):
return by
elif isinstance(by, list):
return [_normalize_by(df, col) for col in by]
elif is_series_like(by) and by.name in df.columns and by._name == df[by.name]._name:
return by.name
elif (
isinstance(by, DataFrame)
and set(by.columns).issubset(df.columns)
and by._name == df[by.columns]._name
):
return list(by.columns)
else:
return by
def _maybe_slice(grouped, columns):
"""
Slice columns if grouped is pd.DataFrameGroupBy
"""
# FIXME: update with better groupby object detection (i.e.: ngroups, get_group)
if "groupby" in type(grouped).__name__.lower():
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 is_series_like(by) or is_dataframe_like(by):
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 does not 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 column 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)
elif by is not None and len(by):
# since we're coming through apply, `by` will be a tuple.
# Pandas treats tuples as a single key, and lists as multiple keys
# We want multiple keys
if isinstance(by, str):
by = [by]
kwargs.update(by=list(by))
return df.groupby(**kwargs)
def _groupby_slice_apply(
df,
grouper,
key,
func,
*args,
group_keys=GROUP_KEYS_DEFAULT,
dropna=None,
observed=None,
**kwargs,
):
# No need to use raise if unaligned here - this is only called after
# shuffling, which makes everything aligned already
dropna = {"dropna": dropna} if dropna is not None else {}
observed = {"observed": observed} if observed is not None else {}
g = df.groupby(grouper, group_keys=group_keys, **observed, **dropna)
if key:
g = g[key]
return g.apply(func, *args, **kwargs)
def _groupby_slice_transform(
df,
grouper,
key,
func,
*args,
group_keys=GROUP_KEYS_DEFAULT,
dropna=None,
observed=None,
**kwargs,
):
# No need to use raise if unaligned here - this is only called after
# shuffling, which makes everything aligned already
dropna = {"dropna": dropna} if dropna is not None else {}
observed = {"observed": observed} if observed is not None else {}
g = df.groupby(grouper, group_keys=group_keys, **observed, **dropna)
if key:
g = g[key]
# Cannot call transform on an empty dataframe
if len(df) == 0:
return g.apply(func, *args, **kwargs)
return g.transform(func, *args, **kwargs)
def _groupby_slice_shift(
df,
grouper,
key,
shuffled,
group_keys=GROUP_KEYS_DEFAULT,
dropna=None,
observed=None,
**kwargs,
):
# No need to use raise if unaligned here - this is only called after
# shuffling, which makes everything aligned already
dropna = {"dropna": dropna} if dropna is not None else {}
observed = {"observed": observed} if observed is not None else {}
if shuffled:
df = df.sort_index()
g = df.groupby(grouper, group_keys=group_keys, **observed, **dropna)
if key:
g = g[key]
return g.shift(**kwargs)
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)
try:
if is_dataframe_like(df):
grouped = grouped[columns]
return grouped.get_group(get_key)
except KeyError:
# to create empty DataFrame/Series, which has the same
# dtype as the original
if is_dataframe_like(df):
# may be SeriesGroupBy
df = df[columns]
return df.iloc[0:0]
###############################################################
# Aggregation
###############################################################
[docs]class Aggregation:
"""User defined groupby-aggregation.
This class allows users to define their own custom aggregation in terms of
operations on Pandas dataframes in a map-reduce style. You need to specify
what operation to do on each chunk of data, how to combine those chunks of
data together, and then how to finalize the result.
See :ref:`dataframe.groupby.aggregate` for more.
Parameters
----------
name : str
the name of the aggregation. It should be unique, since intermediate
result will be identified by this name.
chunk : callable
a function that will be called with the grouped column of each
partition. It can either return a single series or a tuple of series.
The index has to be equal to the groups.
agg : callable
a function that will be called to aggregate the results of each chunk.
Again the argument(s) will be grouped series. If ``chunk`` returned a
tuple, ``agg`` will be called with all of them as individual positional
arguments.
finalize : callable
an optional finalizer that will be called with the results from the
aggregation.
Examples
--------
We could implement ``sum`` as follows:
>>> custom_sum = dd.Aggregation(
... name='custom_sum',
... chunk=lambda s: s.sum(),
... agg=lambda s0: s0.sum()
... ) # doctest: +SKIP
>>> df.groupby('g').agg(custom_sum) # doctest: +SKIP
We can implement ``mean`` as follows:
>>> custom_mean = dd.Aggregation(
... name='custom_mean',
... chunk=lambda s: (s.count(), s.sum()),
... agg=lambda count, sum: (count.sum(), sum.sum()),
... finalize=lambda count, sum: sum / count,
... ) # doctest: +SKIP
>>> df.groupby('g').agg(custom_mean) # doctest: +SKIP
Though of course, both of these are built-in and so you don't need to
implement them yourself.
"""
[docs] def __init__(self, name, chunk, agg, finalize=None):
self.chunk = chunk
self.agg = agg
self.finalize = finalize
self.__name__ = name
def _groupby_aggregate(
df, aggfunc=None, levels=None, dropna=None, sort=False, observed=None, **kwargs
):
dropna = {"dropna": dropna} if dropna is not None else {}
observed = {"observed": observed} if observed is not None else {}
grouped = df.groupby(level=levels, sort=sort, **observed, **dropna)
return aggfunc(grouped, **kwargs)
def _apply_chunk(df, *by, dropna=None, observed=None, **kwargs):
func = kwargs.pop("chunk")
columns = kwargs.pop("columns")
dropna = {"dropna": dropna} if dropna is not None else {}
observed = {"observed": observed} if observed is not None else {}
g = _groupby_raise_unaligned(df, by=by, **observed, **dropna)
if is_series_like(df) or columns is None:
return func(g, **kwargs)
else:
if isinstance(columns, (tuple, list, set, pd.Index)):
columns = list(columns)
return func(g[columns], **kwargs)
def _var_chunk(df, *by):
if is_series_like(df):
df = df.to_frame()
df = df.copy()
g = _groupby_raise_unaligned(df, by=by)
with check_numeric_only_deprecation():
x = g.sum()
n = g[x.columns].count().rename(columns=lambda c: (c, "-count"))
cols = x.columns
df[cols] = df[cols] ** 2
g2 = _groupby_raise_unaligned(df, by=by)
with check_numeric_only_deprecation():
x2 = g2.sum().rename(columns=lambda c: (c, "-x2"))
return concat([x, x2, n], axis=1)
def _var_combine(g, levels, sort=False):
return g.groupby(level=levels, sort=sort).sum()
def _var_agg(g, levels, ddof, sort=False):
g = g.groupby(level=levels, sort=sort).sum()
nc = len(g.columns)
x = g[g.columns[: nc // 3]]
# chunks columns are tuples (value, name), so we just keep the value part
x2 = g[g.columns[nc // 3 : 2 * nc // 3]].rename(columns=lambda c: c[0])
n = g[g.columns[-nc // 3 :]].rename(columns=lambda c: c[0])
# 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 is_dataframe_like(result)
result[result < 0] = 0 # avoid rounding errors that take us to zero
return result
def _cov_combine(g, levels):
return g
def _cov_finalizer(df, cols, std=False):
vals = []
num_elements = len(list(it.product(cols, repeat=2)))
num_cols = len(cols)
vals = list(range(num_elements))
col_idx_mapping = dict(zip(cols, range(num_cols)))
for i, j in it.combinations_with_replacement(df[cols].columns, 2):
x = col_idx_mapping[i]
y = col_idx_mapping[j]
idx = x + num_cols * y
mul_col = f"{i}{j}"
ni = df["%s-count" % i]
nj = df["%s-count" % j]
n = np.sqrt(ni * nj)
div = n - 1
div[div < 0] = 0
val = (df[mul_col] - df[i] * df[j] / n).values[0] / div.values[0]
if std:
ii = f"{i}{i}"
jj = f"{j}{j}"
std_val_i = (df[ii] - (df[i] ** 2) / ni).values[0] / div.values[0]
std_val_j = (df[jj] - (df[j] ** 2) / nj).values[0] / div.values[0]
val = val / np.sqrt(std_val_i * std_val_j)
vals[idx] = val
if i != j:
idx = num_cols * x + y
vals[idx] = val
level_1 = cols
index = pd.MultiIndex.from_product([level_1, level_1])
return pd.Series(vals, index=index)
def _mul_cols(df, cols):
"""Internal function to be used with apply to multiply
each column in a dataframe by every other column
a b c -> a*a, a*b, b*b, b*c, c*c
"""
_df = df.__class__()
for i, j in it.combinations_with_replacement(cols, 2):
col = f"{i}{j}"
_df[col] = df[i] * df[j]
# Fix index in a groupby().apply() context
# https://github.com/dask/dask/issues/8137
# https://github.com/pandas-dev/pandas/issues/43568
_df.index = [0] * len(_df)
return _df
def _cov_chunk(df, *by):
"""Covariance Chunk Logic
Parameters
----------
df : Pandas.DataFrame
std : bool, optional
When std=True we are calculating with Correlation
Returns
-------
tuple
Processed X, Multiplied Cols,
"""
if is_series_like(df):
df = df.to_frame()
df = df.copy()
# mapping columns to str(numerical) values allows us to easily handle
# arbitrary column names (numbers, string, empty strings)
col_mapping = collections.OrderedDict()
for i, c in enumerate(df.columns):
col_mapping[c] = str(i)
df = df.rename(columns=col_mapping)
cols = df._get_numeric_data().columns
# when grouping by external series don't exclude columns
is_mask = any(is_series_like(s) for s in by)
if not is_mask:
by = [col_mapping[k] for k in by]
cols = cols.drop(np.array(by))
g = _groupby_raise_unaligned(df, by=by)
x = g.sum()
mul = g.apply(_mul_cols, cols=cols).reset_index(level=-1, drop=True)
n = g[x.columns].count().rename(columns=lambda c: f"{c}-count")
return (x, mul, n, col_mapping)
def _cov_agg(_t, levels, ddof, std=False, sort=False):
sums = []
muls = []
counts = []
# sometime we get a series back from concat combiner
t = list(_t)
cols = t[0][0].columns
for x, mul, n, col_mapping in t:
sums.append(x)
muls.append(mul)
counts.append(n)
col_mapping = col_mapping
total_sums = concat(sums).groupby(level=levels, sort=sort).sum()
total_muls = concat(muls).groupby(level=levels, sort=sort).sum()
total_counts = concat(counts).groupby(level=levels).sum()
result = (
concat([total_sums, total_muls, total_counts], axis=1)
.groupby(level=levels)
.apply(_cov_finalizer, cols=cols, std=std)
)
inv_col_mapping = {v: k for k, v in col_mapping.items()}
idx_vals = result.index.names
idx_mapping = list()
# when index is None we probably have selected a particular column
# df.groupby('a')[['b']].cov()
if len(idx_vals) == 1 and all(n is None for n in idx_vals):
idx_vals = list(inv_col_mapping.keys() - set(total_sums.columns))
for val in idx_vals:
idx_name = inv_col_mapping.get(val, val)
idx_mapping.append(idx_name)
if len(result.columns.levels[0]) < len(col_mapping):
# removing index from col_mapping (produces incorrect multiindexes)
try:
col_mapping.pop(idx_name)
except KeyError:
# when slicing the col_map will not have the index
pass
keys = list(col_mapping.keys())
for level in range(len(result.columns.levels)):
result.columns = result.columns.set_levels(keys, level=level)
result.index.set_names(idx_mapping, inplace=True)
# stacking can lead to a sorted index
s_result = result.stack(dropna=False)
assert is_dataframe_like(s_result)
return s_result
###############################################################
# nunique
###############################################################
def _drop_duplicates_reindex(df):
# Fix index in a groupby().apply() context
# https://github.com/dask/dask/issues/8137
# https://github.com/pandas-dev/pandas/issues/43568
result = df.drop_duplicates()
result.index = [0] * len(result)
return result
def _nunique_df_chunk(df, *by, **kwargs):
name = kwargs.pop("name")
g = _groupby_raise_unaligned(df, by=by)
if len(df) > 0:
grouped = (
g[[name]].apply(_drop_duplicates_reindex).reset_index(level=-1, drop=True)
)
else:
# Manually create empty version, since groupby-apply for empty frame
# results in df with no columns
grouped = g[[name]].nunique()
grouped = grouped.astype(df.dtypes[grouped.columns].to_dict())
return grouped
def _nunique_df_combine(df, levels, sort=False):
result = (
df.groupby(level=levels, sort=sort)
.apply(_drop_duplicates_reindex)
.reset_index(level=-1, drop=True)
)
return result
def _nunique_df_aggregate(df, levels, name, sort=False):
return df.groupby(level=levels, sort=sort)[name].nunique()
def _nunique_series_chunk(df, *by, **_ignored_):
# convert series to data frame, then hand over to dataframe code path
assert is_series_like(df)
df = df.to_frame()
kwargs = dict(name=df.columns[0], levels=_determine_levels(by))
return _nunique_df_chunk(df, *by, **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 f"{func!s}-{column!s}-{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(f"unsupported agg spec of type {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 argument 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, keyword) 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"}
# check that there are no name conflicts for a single input column
by_name = {}
for _, func, input_column in spec:
key = funcname(known_np_funcs.get(func, func)), input_column
by_name.setdefault(key, []).append((func, input_column))
for funcs in by_name.values():
if len(funcs) != 1:
raise ValueError(f"conflicting aggregation functions: {funcs}")
chunks = {}
aggs = {}
finalizers = []
for (result_column, func, input_column) in spec:
if not isinstance(func, Aggregation):
func = funcname(known_np_funcs.get(func, func))
impls = _build_agg_args_single(result_column, func, input_column)
# overwrite existing result-columns, generate intermediates only once
for spec in impls["chunk_funcs"]:
chunks[spec[0]] = spec
for spec in impls["aggregate_funcs"]:
aggs[spec[0]] = spec
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),
"first": (M.first, M.first),
"last": (M.last, M.last),
"prod": (M.prod, M.prod),
}
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)
elif func == "list":
return _build_agg_args_list(result_column, func, input_column)
elif isinstance(func, Aggregation):
return _build_agg_args_custom(result_column, func, input_column)
else:
raise ValueError(f"unknown aggregate {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, 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 _build_agg_args_list(result_column, func, input_column):
intermediate = _make_agg_id("list", input_column)
return dict(
chunk_funcs=[
(
intermediate,
_apply_func_to_column,
dict(column=input_column, func=lambda s: s.apply(list)),
)
],
aggregate_funcs=[
(
intermediate,
_apply_func_to_column,
dict(
column=intermediate,
func=lambda s0: s0.apply(
lambda chunks: list(it.chain.from_iterable(chunks))
),
),
)
],
finalizer=(result_column, itemgetter(intermediate), dict()),
)
def _build_agg_args_custom(result_column, func, input_column):
col = _make_agg_id(funcname(func), input_column)
if func.finalize is None:
finalizer = (result_column, operator.itemgetter(col), dict())
else:
finalizer = (
result_column,
_apply_func_to_columns,
dict(func=func.finalize, prefix=col),
)
return dict(
chunk_funcs=[
(col, _apply_func_to_column, dict(func=func.chunk, column=input_column))
],
aggregate_funcs=[
(col, _apply_func_to_columns, dict(func=func.agg, prefix=col))
],
finalizer=finalizer,
)
def _groupby_apply_funcs(df, *by, **kwargs):
"""
Group a dataframe and apply multiple aggregation functions.
Parameters
----------
df: pandas.DataFrame
The dataframe to work on.
by: 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(by):
# since we're coming through apply, `by` will be a tuple.
# Pandas treats tuples as a single key, and lists as multiple keys
# We want multiple keys
kwargs.update(by=list(by))
funcs = kwargs.pop("funcs")
grouped = _groupby_raise_unaligned(df, **kwargs)
result = collections.OrderedDict()
for result_column, func, func_kwargs in funcs:
r = func(grouped, **func_kwargs)
if isinstance(r, tuple):
for idx, s in enumerate(r):
result[f"{result_column}-{idx}"] = s
else:
result[result_column] = r
if is_dataframe_like(df):
return df.__class__(result)
else:
# Get the DataFrame type of this Series object
return df.head(0).to_frame().__class__(result)
def _compute_sum_of_squares(grouped, column):
# Note: CuDF cannot use `groupby.apply`.
# Need to unpack groupby to compute sum of squares
if hasattr(grouped, "grouper"):
keys = grouped.grouper
else:
# Handle CuDF groupby object (different from pandas)
keys = grouped.grouping.keys
df = grouped.obj[column].pow(2) if column else grouped.obj.pow(2)
return df.groupby(keys).sum()
def _agg_finalize(df, aggregate_funcs, finalize_funcs, level, sort=False, **kwargs):
# finish the final aggregation level
df = _groupby_apply_funcs(
df, funcs=aggregate_funcs, level=level, sort=sort, **kwargs
)
# and finalize the result
result = collections.OrderedDict()
for result_column, func, finalize_kwargs in finalize_funcs:
result[result_column] = func(df, **finalize_kwargs)
return df.__class__(result)
def _apply_func_to_column(df_like, column, func):
if column is None:
return func(df_like)
return func(df_like[column])
def _apply_func_to_columns(df_like, prefix, func):
if is_dataframe_like(df_like):
columns = df_like.columns
else:
# handle GroupBy objects
columns = df_like.obj.columns
columns = sorted(col for col in columns if col.startswith(prefix))
columns = [df_like[col] for col in columns]
return func(*columns)
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
def _fillna_group(group, by, value, method, limit, fillna_axis):
# apply() conserves the grouped-by columns, so drop them to stay consistent with pandas groupby-fillna
return group.drop(columns=by).fillna(
value=value, method=method, limit=limit, axis=fillna_axis
)
def _aggregate_docstring(based_on=None):
# Insert common groupby-aggregation docstring.
# Use `based_on` parameter to add note about the
# Pandas method the Dask version is based on.
based_on_str = "\n" if based_on is None else f"\nBased on {based_on}\n"
def wrapper(func):
func.__doc__ = f"""Aggregate using one or more specified operations
{based_on_str}
Parameters
----------
arg : callable, str, list or dict
Aggregation spec. Accepted combinations are:
- callable function
- string function name
- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
- dict of column names -> function, function name or list of such.
split_every : int, optional
Number of intermediate partitions that may be aggregated at once.
This defaults to 8. If your intermediate partitions are likely to
be small (either due to a small number of groups or a small initial
partition size), consider increasing this number for better performance.
split_out : int, optional
Number of output partitions. Default is 1.
shuffle : bool or str, optional
Whether a shuffle-based algorithm should be used. A specific
algorithm name may also be specified (e.g. ``"tasks"`` or ``"p2p"``).
The shuffle-based algorithm is likely to be more efficient than
``shuffle=False`` when ``split_out>1`` and the number of unique
groups is large (high cardinality). Default is ``False`` when
``split_out = 1``. When ``split_out > 1``, it chooses the algorithm
set by the ``shuffle`` option in the dask config system, or ``"tasks"``
if nothing is set.
"""
return func
return wrapper
class _GroupBy:
"""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
group_keys: bool | None
Passed to pandas.DataFrame.groupby()
dropna: bool
Whether to drop null values from groupby index
sort: bool
Passed along to aggregation methods. If allowed,
the output aggregation will have sorted keys.
observed: bool, default False
This only applies if any of the groupers are Categoricals.
If True: only show observed values for categorical groupers.
If False: show all values for categorical groupers.
"""
def __init__(
self,
df,
by=None,
slice=None,
group_keys=GROUP_KEYS_DEFAULT,
dropna=None,
sort=True,
observed=False,
):
by_ = by if isinstance(by, (tuple, list)) else [by]
if any(isinstance(key, pd.Grouper) for key in by_):
raise NotImplementedError("pd.Grouper is currently not supported by Dask.")
assert isinstance(df, (DataFrame, Series))
self.group_keys = group_keys
self.obj = df
# grouping key passed via groupby method
self.by = _normalize_by(df, by)
self.sort = sort
partitions_aligned = all(
item.npartitions == df.npartitions if isinstance(item, Series) else True
for item in (self.by if isinstance(self.by, (tuple, list)) else [self.by])
)
if not partitions_aligned:
raise NotImplementedError(
"The grouped object and 'by' of the groupby must have the same divisions."
)
# slicing key applied to _GroupBy instance
self._slice = slice
if isinstance(self.by, list):
by_meta = [
item._meta if isinstance(item, Series) else item for item in self.by
]
elif isinstance(self.by, Series):
by_meta = self.by._meta
else:
by_meta = self.by
self.dropna = {}
if dropna is not None:
self.dropna["dropna"] = dropna
# Hold off on setting observed by default: https://github.com/dask/dask/issues/6951
self.observed = {}
if observed is not None:
self.observed["observed"] = observed
self._meta = self.obj._meta.groupby(
by_meta, group_keys=group_keys, **self.observed, **self.dropna
)
@property # type: ignore
@_deprecated()
def index(self):
return self.by
@index.setter
def index(self, value):
self.by = value
@property
def _groupby_kwargs(self):
return {
"by": self.by,
"group_keys": self.group_keys,
**self.dropna,
"sort": self.sort,
**self.observed,
}
def __iter__(self):
raise NotImplementedError(
"Iteration of DataFrameGroupBy objects requires computing the groups which "
"may be slow. You probably want to use 'apply' to execute a function for "
"all the columns. To access individual groups, use 'get_group'. To list "
"all the group names, use 'df[<group column>].unique().compute()'."
)
@property
def _meta_nonempty(self):
"""
Return a pd.DataFrameGroupBy / pd.SeriesGroupBy which contains sample data.
"""
sample = self.obj._meta_nonempty
if isinstance(self.by, list):
by_meta = [
item._meta_nonempty if isinstance(item, Series) else item
for item in self.by
]
elif isinstance(self.by, Series):
by_meta = self.by._meta_nonempty
else:
by_meta = self.by
grouped = sample.groupby(
by_meta,
group_keys=self.group_keys,
**self.observed,
**self.dropna,
)
return _maybe_slice(grouped, self._slice)
def _aca_agg(
self,
token,
func,
aggfunc=None,
meta=None,
split_every=None,
split_out=1,
chunk_kwargs=None,
aggregate_kwargs=None,
):
if self.sort is None and split_out > 1:
warnings.warn(SORT_SPLIT_OUT_WARNING, FutureWarning)
if aggfunc is None:
aggfunc = func
if meta is None:
with check_numeric_only_deprecation():
meta = func(self._meta_nonempty)
if chunk_kwargs is None:
chunk_kwargs = {}
if aggregate_kwargs is None:
aggregate_kwargs = {}
columns = meta.name if is_series_like(meta) else meta.columns
token = self._token_prefix + token
levels = _determine_levels(self.by)
return aca(
[self.obj, self.by]
if not isinstance(self.by, list)
else [self.obj] + self.by,
chunk=_apply_chunk,
chunk_kwargs=dict(
chunk=func,
columns=columns,
**self.observed,
**chunk_kwargs,
**self.dropna,
),
aggregate=_groupby_aggregate,
meta=meta,
token=token,
split_every=split_every,
aggregate_kwargs=dict(
aggfunc=aggfunc,
levels=levels,
**self.observed,
**aggregate_kwargs,
**self.dropna,
),
split_out=split_out,
split_out_setup=split_out_on_index,
sort=self.sort,
)
def _cum_agg(self, token, chunk, aggregate, initial):
"""Wrapper for cumulative groupby operation"""
meta = chunk(self._meta)
columns = meta.name if is_series_like(meta) else meta.columns
by_cols = self.by if isinstance(self.by, list) else [self.by]
# rename "by" columns internally
# to fix cumulative operations on the same "by" columns
# ref: https://github.com/dask/dask/issues/9313
if columns is not None and set(columns).intersection(set(by_cols)):
by = []
for col in by_cols:
suffix = str(uuid.uuid4())
self.obj = self.obj.assign(**{col + suffix: self.obj[col]})
by.append(col + suffix)
else:
by = by_cols
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,
*by,
chunk=chunk,
columns=columns,
token=name_part,
meta=meta,
**self.dropna,
)
cumpart_raw_frame = (
cumpart_raw.to_frame() if is_series_like(meta) else cumpart_raw
)
cumpart_ext = cumpart_raw_frame.assign(
**{
i: self.obj[i]
if np.isscalar(i) and i in getattr(self.obj, "columns", [])
else self.obj.index
for i in by
}
)
# Use pd.Grouper objects to specify that we are grouping by columns.
# Otherwise, pandas will throw an ambiguity warning if the
# DataFrame's index (self.obj.index) was included in the grouping
# specification (self.by). See pandas #14432
grouper = grouper_dispatch(self._meta.obj)
by_groupers = [grouper(key=ind) for ind in by]
cumlast = map_partitions(
_apply_chunk,
cumpart_ext,
*by_groupers,
columns=0 if columns is None else columns,
chunk=M.last,
meta=meta,
token=name_last,
**self.dropna,
)
# aggregate cumulated partitions and its previous last element
_hash = tokenize(self, token, chunk, aggregate, initial)
name += "-" + _hash
name_cum += "-" + _hash
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),
by,
0 if columns is None else columns,
aggregate,
initial,
)
dependencies = [cumpart_raw]
if self.obj.npartitions > 1:
dependencies += [cumpart_ext, cumlast]
graph = HighLevelGraph.from_collections(name, dask, dependencies=dependencies)
return new_dd_object(graph, name, chunk(self._meta), self.obj.divisions)
def compute(self, **kwargs):
raise NotImplementedError(
"DataFrameGroupBy does not allow compute method."
"Please chain it with an aggregation method (like ``.mean()``) or get a "
"specific group using ``.get_group()`` before calling ``compute()``"
)
def _shuffle(self, meta):
df = self.obj
if isinstance(self.obj, Series):
# Temporarily convert series to dataframe for shuffle
df = df.to_frame("__series__")
convert_back_to_series = True
else:
convert_back_to_series = False
if isinstance(self.by, DataFrame): # add by columns to dataframe
df2 = df.assign(**{"_by_" + c: self.by[c] for c in self.by.columns})
by = self.by
elif isinstance(self.by, Series):
df2 = df.assign(_by=self.by)
by = self.by
else:
df2 = df
by = df._select_columns_or_index(self.by)
df3 = shuffle(df2, by) # shuffle dataframe and index
if isinstance(self.by, DataFrame):
# extract by from dataframe
cols = ["_by_" + c for c in self.by.columns]
by2 = df3[cols]
if is_dataframe_like(meta):
df4 = df3.map_partitions(drop_columns, cols, meta.columns.dtype)
else:
df4 = df3.drop(cols, axis=1)
elif isinstance(self.by, Series):
by2 = df3["_by"]
by2.name = self.by.name
if is_dataframe_like(meta):
df4 = df3.map_partitions(drop_columns, "_by", meta.columns.dtype)
else:
df4 = df3.drop("_by", axis=1)
else:
df4 = df3
by2 = self.by
if convert_back_to_series:
df4 = df4["__series__"].rename(self.obj.name)
return df4, by2
@derived_from(pd.core.groupby.GroupBy)
def cumsum(self, axis=0):
if axis:
if isinstance(self, SeriesGroupBy):
raise ValueError("No axis named 1 for object type Series")
else:
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:
if isinstance(self, SeriesGroupBy):
raise ValueError("No axis named 1 for object type Series")
else:
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=no_default):
if axis is not no_default:
warnings.warn(
"The `axis` keyword argument is deprecated and will removed in a future release. "
"Previously it was unused and had no effect.",
FutureWarning,
)
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, min_count=None):
result = self._aca_agg(
token="sum", func=M.sum, split_every=split_every, split_out=split_out
)
if min_count:
return result.where(self.count() >= min_count, other=np.NaN)
else:
return result
@derived_from(pd.core.groupby.GroupBy)
def prod(self, split_every=None, split_out=1, min_count=None):
result = self._aca_agg(
token="prod", func=M.prod, split_every=split_every, split_out=split_out
)
if min_count:
return result.where(self.count() >= min_count, other=np.NaN)
else:
return result
@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.DataFrame)
def idxmin(self, split_every=None, split_out=1, axis=None, skipna=True):
return self._aca_agg(
token="idxmin",
func=M.idxmin,
aggfunc=M.first,
split_every=split_every,
split_out=split_out,
chunk_kwargs=dict(skipna=skipna),
)
@derived_from(pd.DataFrame)
def idxmax(self, split_every=None, split_out=1, axis=None, skipna=True):
return self._aca_agg(
token="idxmax",
func=M.idxmax,
aggfunc=M.first,
split_every=split_every,
split_out=split_out,
chunk_kwargs=dict(skipna=skipna),
)
@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):
s = self.sum(split_every=split_every, split_out=split_out)
c = self.count(split_every=split_every, split_out=split_out)
if is_dataframe_like(s):
c = c[s.columns]
return s / c
@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):
if self.sort is None and split_out > 1:
warnings.warn(SORT_SPLIT_OUT_WARNING, FutureWarning)
levels = _determine_levels(self.by)
result = aca(
[self.obj, self.by]
if not isinstance(self.by, list)
else [self.obj] + self.by,
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,
sort=self.sort,
)
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.DataFrame)
def corr(self, ddof=1, split_every=None, split_out=1):
"""Groupby correlation:
corr(X, Y) = cov(X, Y) / (std_x * std_y)
"""
return self.cov(split_every=split_every, split_out=split_out, std=True)
@derived_from(pd.DataFrame)
def cov(self, ddof=1, split_every=None, split_out=1, std=False):
"""Groupby covariance is accomplished by
1. Computing intermediate values for sum, count, and the product of
all columns: a b c -> a*a, a*b, b*b, b*c, c*c.
2. The values are then aggregated and the final covariance value is calculated:
cov(X, Y) = X*Y - Xbar * Ybar
When `std` is True calculate Correlation
"""
if self.sort is None and split_out > 1:
warnings.warn(SORT_SPLIT_OUT_WARNING, FutureWarning)
levels = _determine_levels(self.by)
is_mask = any(is_series_like(s) for s in self.by)
if self._slice:
if is_mask:
self.obj = self.obj[self._slice]
else:
sliced_plus = list(self._slice) + list(self.by)
self.obj = self.obj[sliced_plus]
result = aca(
[self.obj, self.by]
if not isinstance(self.by, list)
else [self.obj] + self.by,
chunk=_cov_chunk,
aggregate=_cov_agg,
combine=_cov_combine,
token=self._token_prefix + "cov",
aggregate_kwargs={"ddof": ddof, "levels": levels, "std": std},
combine_kwargs={"levels": levels},
split_every=split_every,
split_out=split_out,
split_out_setup=split_out_on_index,
sort=self.sort,
)
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 first(self, split_every=None, split_out=1):
return self._aca_agg(
token="first", func=M.first, split_every=split_every, split_out=split_out
)
@derived_from(pd.core.groupby.GroupBy)
def last(self, split_every=None, split_out=1):
return self._aca_agg(
token="last", func=M.last, split_every=split_every, split_out=split_out
)
@derived_from(
pd.core.groupby.GroupBy,
inconsistencies="If the group is not present, Dask will return an empty Series/DataFrame.",
)
def get_group(self, key):
token = self._token_prefix + "get_group"
meta = self._meta.obj
if is_dataframe_like(meta) and self._slice is not None:
meta = meta[self._slice]
columns = meta.columns if is_dataframe_like(meta) else meta.name
return map_partitions(
_groupby_get_group,
self.obj,
self.by,
key,
columns,
meta=meta,
token=token,
)
@_aggregate_docstring()
def aggregate(self, arg, split_every=None, split_out=1, shuffle=None):
if split_out is None:
warnings.warn(
"split_out=None is deprecated, please use a positive integer, "
"or allow the default of 1",
category=FutureWarning,
)
split_out = 1
if shuffle is None:
if split_out > 1:
shuffle = shuffle or config.get("shuffle", None) or "tasks"
else:
shuffle = False
column_projection = None
if isinstance(self.obj, DataFrame):
if isinstance(self.by, tuple) or np.isscalar(self.by):
group_columns = {self.by}
elif isinstance(self.by, list):
group_columns = {
i for i in self.by 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 by normalization to replace
# series with their name.
non_group_columns = [
col for col in self.obj.columns if col not in group_columns
]
spec = _normalize_spec(arg, non_group_columns)
# Check if the aggregation involves implicit column projection
if isinstance(arg, dict):
column_projection = group_columns | arg.keys()
elif isinstance(self.obj, Series):
if isinstance(arg, (list, tuple, dict)):
# 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:
spec = _normalize_spec({None: arg}, [])
spec = [
(self.obj.name, func, input_column)
for (_, func, input_column) in spec
]
else:
raise ValueError(f"aggregate on unknown object {self.obj}")
chunk_funcs, aggregate_funcs, finalizers = _build_agg_args(spec)
if isinstance(self.by, (tuple, list)) and len(self.by) > 1:
levels = list(range(len(self.by)))
else:
levels = 0
# Add an explicit `getitem` operation if the groupby
# aggregation involves implicit column projection.
# This makes it possible for the column-projection
# to be pushed into the IO layer
_obj = self.obj[list(column_projection)] if column_projection else self.obj
if not isinstance(self.by, list):
chunk_args = [_obj, self.by]
else:
chunk_args = [_obj] + self.by
if not PANDAS_GT_110 and self.dropna:
raise NotImplementedError(
"dropna is not a valid argument for dask.groupby.agg"
f"if pandas < 1.1.0. Pandas version is {pd.__version__}"
)
if shuffle:
# Shuffle-based aggregation
#
# This algorithm is more scalable than a tree reduction
# for larger values of split_out. However, the shuffle
# step requires that the result of `chunk` produces a
# proper DataFrame type
return _shuffle_aggregate(
chunk_args,
chunk=_groupby_apply_funcs,
chunk_kwargs=dict(
funcs=chunk_funcs,
sort=False,
**self.observed,
**self.dropna,
),
aggregate=_agg_finalize,
aggregate_kwargs=dict(
aggregate_funcs=aggregate_funcs,
finalize_funcs=finalizers,
level=levels,
**self.observed,
**self.dropna,
),
token="aggregate",
split_every=split_every,
split_out=split_out,
shuffle=shuffle if isinstance(shuffle, str) else "tasks",
sort=self.sort,
)
if self.sort is None and split_out > 1:
warnings.warn(SORT_SPLIT_OUT_WARNING, FutureWarning)
# Check sort behavior
if self.sort and split_out > 1:
raise NotImplementedError(
"Cannot guarantee sorted keys for `split_out>1` and `shuffle=False`"
" Try using `shuffle=True` if you are grouping on a single column."
" Otherwise, try using split_out=1, or grouping with sort=False."
)
return aca(
chunk_args,
chunk=_groupby_apply_funcs,
chunk_kwargs=dict(
funcs=chunk_funcs,
sort=False,
**self.observed,
**self.dropna,
),
combine=_groupby_apply_funcs,
combine_kwargs=dict(
funcs=aggregate_funcs,
level=levels,
sort=False,
**self.observed,
**self.dropna,
),
aggregate=_agg_finalize,
aggregate_kwargs=dict(
aggregate_funcs=aggregate_funcs,
finalize_funcs=finalizers,
level=levels,
**self.observed,
**self.dropna,
),
token="aggregate",
split_every=split_every,
split_out=split_out,
split_out_setup=split_out_on_index,
sort=self.sort,
)
@insert_meta_param_description(pad=12)
def apply(self, func, *args, **kwargs):
"""Parallel version of pandas GroupBy.apply
This mimics the pandas version except for the following:
1. 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.
2. Dask's GroupBy.apply is not appropriate for aggregations. For custom
aggregations, use :class:`dask.dataframe.groupby.Aggregation`.
.. warning::
Pandas' groupby-apply can be used to to apply arbitrary functions,
including aggregations that result in one row per group. Dask's
groupby-apply will apply ``func`` once on each group, doing a shuffle
if needed, such that each group is contained in one partition.
When ``func`` is a reduction, e.g., you'll end up with one row
per group. To apply a custom aggregation with Dask,
use :class:`dask.dataframe.groupby.Aggregation`.
Parameters
----------
func: function
Function to apply
args, kwargs : Scalar, Delayed or object
Arguments and keywords to pass to the function.
$META
Returns
-------
applied : Series or DataFrame depending on columns keyword
"""
meta = kwargs.get("meta", no_default)
if meta is no_default:
with raise_on_meta_error(f"groupby.apply({funcname(func)})", udf=True):
meta_args, meta_kwargs = _extract_meta((args, kwargs), nonempty=True)
meta = self._meta_nonempty.apply(func, *meta_args, **meta_kwargs)
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)
meta = make_meta(meta, parent_meta=self._meta.obj)
# Validate self.by
if isinstance(self.by, list) and any(
isinstance(item, Series) for item in self.by
):
raise NotImplementedError(
"groupby-apply with a multiple Series is currently not supported"
)
df = self.obj
should_shuffle = not (df.known_divisions and df._contains_index_name(self.by))
if should_shuffle:
df2, by = self._shuffle(meta)
else:
df2 = df
by = self.by
# Perform embarrassingly parallel groupby-apply
kwargs["meta"] = meta
df3 = map_partitions(
_groupby_slice_apply,
df2,
by,
self._slice,
func,
token=funcname(func),
*args,
group_keys=self.group_keys,
**self.observed,
**self.dropna,
**kwargs,
)
return df3
@insert_meta_param_description(pad=12)
def transform(self, func, *args, **kwargs):
"""Parallel version of pandas GroupBy.transform
This mimics the pandas version except for the following:
1. 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.
2. Dask's GroupBy.transform is not appropriate for aggregations. For custom
aggregations, use :class:`dask.dataframe.groupby.Aggregation`.
.. warning::
Pandas' groupby-transform can be used to to apply arbitrary functions,
including aggregations that result in one row per group. Dask's
groupby-transform will apply ``func`` once on each group, doing a shuffle
if needed, such that each group is contained in one partition.
When ``func`` is a reduction, e.g., you'll end up with one row
per group. To apply a custom aggregation with Dask,
use :class:`dask.dataframe.groupby.Aggregation`.
Parameters
----------
func: function
Function to apply
args, kwargs : Scalar, Delayed or object
Arguments and keywords to pass to the function.
$META
Returns
-------
applied : Series or DataFrame depending on columns keyword
"""
meta = kwargs.get("meta", no_default)
if meta is no_default:
with raise_on_meta_error(f"groupby.transform({funcname(func)})", udf=True):
meta_args, meta_kwargs = _extract_meta((args, kwargs), nonempty=True)
meta = self._meta_nonempty.transform(func, *meta_args, **meta_kwargs)
msg = (
"`meta` is not specified, inferred from partial data. "
"Please provide `meta` if the result is unexpected.\n"
" Before: .transform(func)\n"
" After: .transform(func, meta={'x': 'f8', 'y': 'f8'}) for dataframe result\n"
" or: .transform(func, meta=('x', 'f8')) for series result"
)
warnings.warn(msg, stacklevel=2)
meta = make_meta(meta, parent_meta=self._meta.obj)
# Validate self.by
if isinstance(self.by, list) and any(
isinstance(item, Series) for item in self.by
):
raise NotImplementedError(
"groupby-transform with a multiple Series is currently not supported"
)
df = self.obj
should_shuffle = not (df.known_divisions and df._contains_index_name(self.by))
if should_shuffle:
df2, by = self._shuffle(meta)
else:
df2 = df
by = self.by
# Perform embarrassingly parallel groupby-transform
kwargs["meta"] = meta
df3 = map_partitions(
_groupby_slice_transform,
df2,
by,
self._slice,
func,
token=funcname(func),
*args,
group_keys=self.group_keys,
**self.observed,
**self.dropna,
**kwargs,
)
return df3
@insert_meta_param_description(pad=12)
def shift(self, periods=1, freq=None, axis=0, fill_value=None, meta=no_default):
"""Parallel version of pandas GroupBy.shift
This mimics the pandas version except for the following:
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
----------
periods : Delayed, Scalar or int, default 1
Number of periods to shift.
freq : Delayed, Scalar or str, optional
Frequency string.
axis : axis to shift, default 0
Shift direction.
fill_value : Scalar, Delayed or object, optional
The scalar value to use for newly introduced missing values.
$META
Returns
-------
shifted : Series or DataFrame shifted within each group.
Examples
--------
>>> import dask
>>> ddf = dask.datasets.timeseries(freq="1H")
>>> result = ddf.groupby("name").shift(1, meta={"id": int, "x": float, "y": float})
"""
if meta is no_default:
with raise_on_meta_error("groupby.shift()", udf=False):
meta_kwargs = _extract_meta(
{
"periods": periods,
"freq": freq,
"axis": axis,
"fill_value": fill_value,
},
nonempty=True,
)
meta = self._meta_nonempty.shift(**meta_kwargs)
msg = (
"`meta` is not specified, inferred from partial data. "
"Please provide `meta` if the result is unexpected.\n"
" Before: .shift(1)\n"
" After: .shift(1, meta={'x': 'f8', 'y': 'f8'}) for dataframe result\n"
" or: .shift(1, meta=('x', 'f8')) for series result"
)
warnings.warn(msg, stacklevel=2)
meta = make_meta(meta, parent_meta=self._meta.obj)
# Validate self.by
if isinstance(self.by, list) and any(
isinstance(item, Series) for item in self.by
):
raise NotImplementedError(
"groupby-shift with a multiple Series is currently not supported"
)
df = self.obj
should_shuffle = not (df.known_divisions and df._contains_index_name(self.by))
if should_shuffle:
df2, by = self._shuffle(meta)
else:
df2 = df
by = self.by
# Perform embarrassingly parallel groupby-shift
result = map_partitions(
_groupby_slice_shift,
df2,
by,
self._slice,
should_shuffle,
periods=periods,
freq=freq,
axis=axis,
fill_value=fill_value,
token="groupby-shift",
group_keys=self.group_keys,
meta=meta,
**self.observed,
**self.dropna,
)
return result
def rolling(self, window, min_periods=None, center=False, win_type=None, axis=0):
"""Provides rolling transformations.
.. note::
Since MultiIndexes are not well supported in Dask, this method returns a
dataframe with the same index as the original data. The groupby column is
not added as the first level of the index like pandas does.
This method works differently from other groupby methods. It does a groupby
on each partition (plus some overlap). This means that the output has the
same shape and number of partitions as the original.
Parameters
----------
window : str, offset
Size of the moving window. This is the number of observations used
for calculating the statistic. Data must have a ``DatetimeIndex``
min_periods : int, default None
Minimum number of observations in window required to have a value
(otherwise result is NA).
center : boolean, default False
Set the labels at the center of the window.
win_type : string, default None
Provide a window type. The recognized window types are identical
to pandas.
axis : int, default 0
Returns
-------
a Rolling object on which to call a method to compute a statistic
Examples
--------
>>> import dask
>>> ddf = dask.datasets.timeseries(freq="1H")
>>> result = ddf.groupby("name").x.rolling('1D').max()
"""
from dask.dataframe.rolling import RollingGroupby
if isinstance(window, Integral):
raise ValueError(
"Only time indexes are supported for rolling groupbys in dask dataframe. "
"``window`` must be a ``freq`` (e.g. '1H')."
)
if min_periods is not None:
if not isinstance(min_periods, Integral):
raise ValueError("min_periods must be an integer")
if min_periods < 0:
raise ValueError("min_periods must be >= 0")
return RollingGroupby(
self,
window=window,
min_periods=min_periods,
center=center,
win_type=win_type,
axis=axis,
)
def fillna(self, value=None, method=None, limit=None, axis=None):
"""Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, default None
Value to use to fill holes (e.g. 0).
method : {'bfill', 'ffill', None}, default None
Method to use for filling holes in reindexed Series. ffill: propagate last
valid observation forward to next valid. bfill: use next valid observation
to fill gap.
axis : {0 or 'index', 1 or 'columns'}
Axis along which to fill missing values.
limit : int, default None
If method is specified, this is the maximum number of consecutive NaN values
to forward/backward fill. In other words, if there is a gap with more than
this number of consecutive NaNs, it will only be partially filled. If method
is not specified, this is the maximum number of entries along the entire
axis where NaNs will be filled. Must be greater than 0 if not None.
Returns
-------
Series or DataFrame
Object with missing values filled
See also
--------
pandas.core.groupby.DataFrameGroupBy.fillna
"""
if not np.isscalar(value) and value is not None:
raise NotImplementedError(
"groupby-fillna with value=dict/Series/DataFrame is currently not supported"
)
meta = self._meta_nonempty.apply(
_fillna_group,
by=self.by,
value=value,
method=method,
limit=limit,
fillna_axis=axis,
)
result = self.apply(
_fillna_group,
by=self.by,
value=value,
method=method,
limit=limit,
fillna_axis=axis,
meta=meta,
)
if PANDAS_GT_150 and self.group_keys:
return result.map_partitions(M.droplevel, self.by)
return result
@derived_from(pd.core.groupby.GroupBy)
def ffill(self, limit=None):
return self.fillna(method="ffill", limit=limit)
@derived_from(pd.core.groupby.GroupBy)
def bfill(self, limit=None):
return self.fillna(method="bfill", limit=limit)
class DataFrameGroupBy(_GroupBy):
_token_prefix = "dataframe-groupby-"
def __getitem__(self, key):
if isinstance(key, list):
g = DataFrameGroupBy(
self.obj, by=self.by, slice=key, sort=self.sort, **self.dropna
)
else:
g = SeriesGroupBy(
self.obj, by=self.by, slice=key, sort=self.sort, **self.dropna
)
# 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(M.isidentifier, self.obj.columns))
)
)
def __getattr__(self, key):
try:
return self[key]
except KeyError as e:
raise AttributeError(e) from e
[docs] @_aggregate_docstring(based_on="pd.core.groupby.DataFrameGroupBy.aggregate")
def aggregate(self, arg, split_every=None, split_out=1, shuffle=None):
if arg == "size":
return self.size()
return super().aggregate(
arg, split_every=split_every, split_out=split_out, shuffle=shuffle
)
@_aggregate_docstring(based_on="pd.core.groupby.DataFrameGroupBy.agg")
def agg(self, arg, split_every=None, split_out=1, shuffle=None):
return self.aggregate(
arg, split_every=split_every, split_out=split_out, shuffle=shuffle
)
class SeriesGroupBy(_GroupBy):
_token_prefix = "series-groupby-"
def __init__(self, df, by=None, slice=None, observed=None, **kwargs):
# for any non series object, raise pandas-compat error message
# Hold off on setting observed by default: https://github.com/dask/dask/issues/6951
observed = {"observed": observed} if observed is not None else {}
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, **observed)
else:
# raise error from pandas, if applicable
df._meta.groupby(by, **observed)
super().__init__(df, by=by, slice=slice, **observed, **kwargs)
[docs] @derived_from(pd.core.groupby.SeriesGroupBy)
def nunique(self, split_every=None, split_out=1):
"""
Examples
--------
>>> import pandas as pd
>>> import dask.dataframe as dd
>>> d = {'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8]}
>>> df = pd.DataFrame(data=d)
>>> ddf = dd.from_pandas(df, 2)
>>> ddf.groupby(['col1']).col2.nunique().compute()
"""
name = self._meta.obj.name
levels = _determine_levels(self.by)
if isinstance(self.obj, DataFrame):
chunk = _nunique_df_chunk
else:
chunk = _nunique_series_chunk
if self.sort is None and split_out > 1:
warnings.warn(SORT_SPLIT_OUT_WARNING, FutureWarning)
return aca(
[self.obj, self.by]
if not isinstance(self.by, list)
else [self.obj] + self.by,
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,
sort=self.sort,
)
[docs] @_aggregate_docstring(based_on="pd.core.groupby.SeriesGroupBy.aggregate")
def aggregate(self, arg, split_every=None, split_out=1, shuffle=None):
result = super().aggregate(
arg, split_every=split_every, split_out=split_out, shuffle=shuffle
)
if self._slice:
result = result[self._slice]
if not isinstance(arg, (list, dict)) and isinstance(result, DataFrame):
result = result[result.columns[0]]
return result
@_aggregate_docstring(based_on="pd.core.groupby.SeriesGroupBy.agg")
def agg(self, arg, split_every=None, split_out=1, shuffle=None):
return self.aggregate(
arg, split_every=split_every, split_out=split_out, shuffle=shuffle
)
@derived_from(pd.core.groupby.SeriesGroupBy)
def value_counts(self, split_every=None, split_out=1):
return self._aca_agg(
token="value_counts",
func=_value_counts,
aggfunc=_value_counts_aggregate,
split_every=split_every,
split_out=split_out,
)
@derived_from(pd.core.groupby.SeriesGroupBy)
def unique(self, split_every=None, split_out=1):
name = self._meta.obj.name
return self._aca_agg(
token="unique",
func=M.unique,
aggfunc=_unique_aggregate,
aggregate_kwargs={"name": name},
split_every=split_every,
split_out=split_out,
)
@derived_from(pd.core.groupby.SeriesGroupBy)
def tail(self, n=5, split_every=None, split_out=1):
index_levels = len(self.by) if isinstance(self.by, list) else 1
return self._aca_agg(
token="tail",
func=_tail_chunk,
aggfunc=_tail_aggregate,
meta=M.tail(self._meta_nonempty),
chunk_kwargs={"n": n},
aggregate_kwargs={"n": n, "index_levels": index_levels},
split_every=split_every,
split_out=split_out,
)
@derived_from(pd.core.groupby.SeriesGroupBy)
def head(self, n=5, split_every=None, split_out=1):
index_levels = len(self.by) if isinstance(self.by, list) else 1
return self._aca_agg(
token="head",
func=_head_chunk,
aggfunc=_head_aggregate,
meta=M.head(self._meta_nonempty),
chunk_kwargs={"n": n},
aggregate_kwargs={"n": n, "index_levels": index_levels},
split_every=split_every,
split_out=split_out,
)
def _unique_aggregate(series_gb, name=None):
ret = type(series_gb.obj)(
{k: v.explode().unique() for k, v in series_gb}, name=name
)
ret.index.names = series_gb.obj.index.names
return ret
def _value_counts(x, **kwargs):
if len(x):
return M.value_counts(x, **kwargs)
else:
return pd.Series(dtype=int)
def _value_counts_aggregate(series_gb):
to_concat = {k: v.groupby(level=1).sum() for k, v in series_gb}
names = list(series_gb.obj.index.names)
return pd.Series(pd.concat(to_concat, names=names))
def _tail_chunk(series_gb, **kwargs):
keys, groups = zip(*series_gb) if len(series_gb) else ((True,), (series_gb,))
return pd.concat([group.tail(**kwargs) for group in groups], keys=keys)
def _tail_aggregate(series_gb, **kwargs):
levels = kwargs.pop("index_levels")
return series_gb.tail(**kwargs).droplevel(list(range(levels)))
def _head_chunk(series_gb, **kwargs):
keys, groups = zip(*series_gb) if len(series_gb) else ((True,), (series_gb,))
return pd.concat([group.head(**kwargs) for group in groups], keys=keys)
def _head_aggregate(series_gb, **kwargs):
levels = kwargs.pop("index_levels")
return series_gb.head(**kwargs).droplevel(list(range(levels)))
def _shuffle_aggregate(
args,
chunk=None,
aggregate=None,
token=None,
chunk_kwargs=None,
aggregate_kwargs=None,
split_every=None,
split_out=1,
sort=True,
ignore_index=False,
shuffle="tasks",
):
"""Shuffle-based groupby aggregation
This algorithm may be more efficient than ACA for large ``split_out``
values (required for high-cardinality groupby indices), but it also
requires the output of ``chunk`` to be a proper DataFrame object.
Parameters
----------
args :
Positional arguments for the `chunk` function. All `dask.dataframe`
objects should be partitioned and indexed equivalently.
chunk : function [block-per-arg] -> block
Function to operate on each block of data
aggregate : function concatenated-block -> block
Function to operate on the concatenated result of chunk
token : str, optional
The name to use for the output keys.
chunk_kwargs : dict, optional
Keywords for the chunk function only.
aggregate_kwargs : dict, optional
Keywords for the aggregate function only.
split_every : int, optional
Number of partitions to aggregate into a shuffle partition.
Defaults to eight, meaning that the initial partitions are repartitioned
into groups of eight before the shuffle. Shuffling scales with the number
of partitions, so it may be helpful to increase this number as a performance
optimization, but only when the aggregated partition can comfortably
fit in worker memory.
split_out : int, optional
Number of output partitions.
ignore_index : bool, default False
Whether the index can be ignored during the shuffle.
sort : bool
If allowed, sort the keys of the output aggregation.
shuffle : str, default "tasks"
Shuffle option to be used by ``DataFrame.shuffle``.
"""
if chunk_kwargs is None:
chunk_kwargs = dict()
if aggregate_kwargs is None:
aggregate_kwargs = dict()
if not isinstance(args, (tuple, list)):
args = [args]
dfs = [arg for arg in args if isinstance(arg, _Frame)]
npartitions = {arg.npartitions for arg in dfs}
if len(npartitions) > 1:
raise ValueError("All arguments must have same number of partitions")
npartitions = npartitions.pop()
if split_every is None:
split_every = 8
elif split_every is False:
split_every = npartitions
elif split_every < 1 or not isinstance(split_every, Integral):
raise ValueError("split_every must be an integer >= 1")
# Shuffle-based groupby aggregation
chunk_name = f"{token or funcname(chunk)}-chunk"
chunked = map_partitions(
chunk,
*args,
meta=chunk(
*[arg._meta if isinstance(arg, _Frame) else arg for arg in args],
**chunk_kwargs,
),
token=chunk_name,
**chunk_kwargs,
)
shuffle_npartitions = max(
chunked.npartitions // split_every,
split_out,
)
# Handle sort kwarg
if sort is not None:
aggregate_kwargs = aggregate_kwargs or {}
aggregate_kwargs["sort"] = sort
if sort is None and split_out > 1:
idx = set(chunked._meta.columns) - set(chunked._meta.reset_index().columns)
if len(idx) > 1:
warnings.warn(
"In the future, `sort` for groupby operations will default to `True`"
" to match the behavior of pandas. However, `sort=True` does not work"
" with `split_out>1` when grouping by multiple columns. To retain the"
" current behavior for multiple output partitions, set `sort=False`.",
FutureWarning,
)
# Perform global sort or shuffle
if sort and split_out > 1:
cols = set(chunked.columns)
chunked = chunked.reset_index()
index_cols = set(chunked.columns) - cols
if len(index_cols) > 1:
raise NotImplementedError(
"Cannot guarantee sorted keys for `split_out>1` when "
"grouping on multiple columns. "
"Try using split_out=1, or grouping with sort=False."
)
result = chunked.set_index(
list(index_cols),
npartitions=shuffle_npartitions,
shuffle=shuffle,
).map_partitions(aggregate, **aggregate_kwargs)
else:
result = chunked.shuffle(
chunked.index,
ignore_index=ignore_index,
npartitions=shuffle_npartitions,
shuffle=shuffle,
).map_partitions(aggregate, **aggregate_kwargs)
if split_out < shuffle_npartitions:
return result.repartition(npartitions=split_out)
return result