Source code for dask.dataframe.rolling

from __future__ import annotations

import datetime
import warnings
from numbers import Integral

import pandas as pd
from pandas.api.types import is_datetime64_any_dtype
from pandas.core.window import Rolling as pd_Rolling

from dask.array.core import normalize_arg
from dask.base import tokenize
from dask.blockwise import BlockwiseDepDict
from dask.dataframe import methods
from dask.dataframe._compat import check_axis_keyword_deprecation
from dask.dataframe.core import (
from import from_pandas
from dask.dataframe.multi import _maybe_align_partitions
from dask.dataframe.utils import (
from dask.delayed import unpack_collections
from dask.highlevelgraph import HighLevelGraph
from dask.typing import no_default
from dask.utils import M, apply, derived_from, funcname, has_keyword

CombinedOutput = type("CombinedOutput", (tuple,), {})

def _combined_parts(prev_part, current_part, next_part, before, after):
    msg = (
        "Partition size is less than overlapping "
        "window size. Try using ``df.repartition`` "
        "to increase the partition size."

    if prev_part is not None and isinstance(before, Integral):
        if prev_part.shape[0] != before:
            raise NotImplementedError(msg)

    if next_part is not None and isinstance(after, Integral):
        if next_part.shape[0] != after:
            raise NotImplementedError(msg)

    parts = [p for p in (prev_part, current_part, next_part) if p is not None]
    combined = methods.concat(parts)

    return CombinedOutput(
            len(prev_part) if prev_part is not None else None,
            len(next_part) if next_part is not None else None,

def overlap_chunk(func, before, after, *args, **kwargs):
    dfs = [df for df in args if isinstance(df, CombinedOutput)]
    combined, prev_part_length, next_part_length = dfs[0]

    args = [arg[0] if isinstance(arg, CombinedOutput) else arg for arg in args]

    out = func(*args, **kwargs)

    if prev_part_length is None:
        before = None
    if isinstance(before, datetime.timedelta):
        before = prev_part_length

    expansion = None
    if combined.shape[0] != 0:
        expansion = out.shape[0] // combined.shape[0]
    if before and expansion:
        before *= expansion
    if next_part_length is None:
        return out.iloc[before:]
    if isinstance(after, datetime.timedelta):
        after = next_part_length
    if after and expansion:
        after *= expansion
    return out.iloc[before:-after]

[docs]@insert_meta_param_description def map_overlap( func, df, before, after, *args, meta=no_default, enforce_metadata=True, transform_divisions=True, align_dataframes=True, **kwargs, ): """Apply a function to each partition, sharing rows with adjacent partitions. Parameters ---------- func : function The function applied to each partition. If this function accepts the special ``partition_info`` keyword argument, it will receive information on the partition's relative location within the dataframe. df: dd.DataFrame, dd.Series args, kwargs : Positional and keyword arguments to pass to the function. Positional arguments are computed on a per-partition basis, while keyword arguments are shared across all partitions. The partition itself will be the first positional argument, with all other arguments passed *after*. Arguments can be ``Scalar``, ``Delayed``, or regular Python objects. DataFrame-like args (both dask and pandas) will be repartitioned to align (if necessary) before applying the function; see ``align_dataframes`` to control this behavior. enforce_metadata : bool, default True Whether to enforce at runtime that the structure of the DataFrame produced by ``func`` actually matches the structure of ``meta``. This will rename and reorder columns for each partition, and will raise an error if this doesn't work, but it won't raise if dtypes don't match. before : int, timedelta or string timedelta The rows to prepend to partition ``i`` from the end of partition ``i - 1``. after : int, timedelta or string timedelta The rows to append to partition ``i`` from the beginning of partition ``i + 1``. transform_divisions : bool, default True Whether to apply the function onto the divisions and apply those transformed divisions to the output. align_dataframes : bool, default True Whether to repartition DataFrame- or Series-like args (both dask and pandas) so their divisions align before applying the function. This requires all inputs to have known divisions. Single-partition inputs will be split into multiple partitions. If False, all inputs must have either the same number of partitions or a single partition. Single-partition inputs will be broadcast to every partition of multi-partition inputs. $META See Also -------- dd.DataFrame.map_overlap """ df = ( from_pandas(df, 1) if (is_series_like(df) or is_dataframe_like(df)) and not is_dask_collection(df) else df ) args = (df,) + args if isinstance(before, str): before = pd.to_timedelta(before) if isinstance(after, str): after = pd.to_timedelta(after) if isinstance(before, datetime.timedelta) or isinstance(after, datetime.timedelta): if not is_datetime64_any_dtype(df.index._meta_nonempty.inferred_type): raise TypeError( "Must have a `DatetimeIndex` when using string offset " "for `before` and `after`" ) else: if not ( isinstance(before, Integral) and before >= 0 and isinstance(after, Integral) and after >= 0 ): raise ValueError("before and after must be positive integers") name = kwargs.pop("token", None) parent_meta = kwargs.pop("parent_meta", None) assert callable(func) if name is not None: token = tokenize(meta, before, after, *args, **kwargs) else: name = "overlap-" + funcname(func) token = tokenize(func, meta, before, after, *args, **kwargs) name = f"{name}-{token}" if align_dataframes: args = _maybe_from_pandas(args) try: args = _maybe_align_partitions(args) except ValueError as e: raise ValueError( f"{e}. If you don't want the partitions to be aligned, and are " "calling `map_overlap` directly, pass `align_dataframes=False`." ) from e dfs = [df for df in args if isinstance(df, _Frame)] meta = _get_meta_map_partitions(args, dfs, func, kwargs, meta, parent_meta) if all(isinstance(arg, Scalar) for arg in args): layer = { (name, 0): ( apply, func, (tuple, [(arg._name, 0) for arg in args]), kwargs, ) } graph = HighLevelGraph.from_collections(name, layer, dependencies=args) return Scalar(graph, name, meta) args2 = [] dependencies = [] divisions = _get_divisions_map_partitions( align_dataframes, transform_divisions, dfs, func, args, kwargs ) def _handle_frame_argument(arg): dsk = {} prevs_parts_dsk, prevs = _get_previous_partitions(arg, before) dsk.update(prevs_parts_dsk) nexts_parts_dsk, nexts = _get_nexts_partitions(arg, after) dsk.update(nexts_parts_dsk) name_a = "overlap-concat-" + tokenize(arg) for i, (prev, current, next) in enumerate( zip(prevs, arg.__dask_keys__(), nexts) ): key = (name_a, i) dsk[key] = (_combined_parts, prev, current, next, before, after) graph = HighLevelGraph.from_collections(name_a, dsk, dependencies=[arg]) return new_dd_object(graph, name_a, meta, divisions) for arg in args: if isinstance(arg, _Frame): arg = _handle_frame_argument(arg) args2.append(arg) dependencies.append(arg) continue arg = normalize_arg(arg) arg2, collections = unpack_collections(arg) if collections: args2.append(arg2) dependencies.extend(collections) else: args2.append(arg) kwargs3 = {} simple = True for k, v in kwargs.items(): v = normalize_arg(v) v, collections = unpack_collections(v) dependencies.extend(collections) kwargs3[k] = v if collections: simple = False if has_keyword(func, "partition_info"): partition_info = { (i,): {"number": i, "division": division} for i, division in enumerate(divisions[:-1]) } args2.insert(0, BlockwiseDepDict(partition_info)) orig_func = func def func(partition_info, *args, **kwargs): return orig_func(*args, **kwargs, partition_info=partition_info) if enforce_metadata: dsk = partitionwise_graph( apply_and_enforce, name, func, before, after, *args2, dependencies=dependencies, _func=overlap_chunk, _meta=meta, **kwargs3, ) else: kwargs4 = kwargs if simple else kwargs3 dsk = partitionwise_graph( overlap_chunk, name, func, before, after, *args2, **kwargs4, dependencies=dependencies, ) graph = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies) return new_dd_object(graph, name, meta, divisions)
def _get_nexts_partitions(df, after): """ Helper to get the nexts partitions required for the overlap """ dsk = {} df_name = df._name timedelta_partition_message = ( "Partition size is less than specified window. " "Try using ``df.repartition`` to increase the partition size" ) name_b = "overlap-append-" + tokenize(df, after) if after and isinstance(after, Integral): nexts = [] for i in range(1, df.npartitions): key = (name_b, i) dsk[key] = (M.head, (df_name, i), after) nexts.append(key) nexts.append(None) elif isinstance(after, datetime.timedelta): # TODO: Do we have a use-case for this? Pandas doesn't allow negative rolling windows deltas = pd.Series(df.divisions).diff().iloc[1:-1] if (after > deltas).any(): raise ValueError(timedelta_partition_message) nexts = [] for i in range(1, df.npartitions): key = (name_b, i) dsk[key] = (_head_timedelta, (df_name, i - 0), (df_name, i), after) nexts.append(key) nexts.append(None) else: nexts = [None] * df.npartitions return dsk, nexts def _get_previous_partitions(df, before): """ Helper to get the previous partitions required for the overlap """ dsk = {} df_name = df._name name_a = "overlap-prepend-" + tokenize(df, before) if before and isinstance(before, Integral): prevs = [None] for i in range(df.npartitions - 1): key = (name_a, i) dsk[key] = (M.tail, (df_name, i), before) prevs.append(key) elif isinstance(before, datetime.timedelta): # Assumes monotonic (increasing?) index divs = pd.Series(df.divisions) deltas = divs.diff().iloc[1:-1] # In the first case window-size is larger than at least one partition, thus it is # necessary to calculate how many partitions must be used for each rolling task. # Otherwise, these calculations can be skipped (faster) if (before > deltas).any(): pt_z = divs[0] prevs = [None] for i in range(df.npartitions - 1): # Select all indexes of relevant partitions between the current partition and # the partition with the highest division outside the rolling window (before) pt_i = divs[i + 1] # lower-bound the search to the first division lb = max(pt_i - before, pt_z) first, j = divs[i], i while first > lb and j > 0: first = first - deltas[j] j = j - 1 key = (name_a, i) dsk[key] = ( _tail_timedelta, [(df_name, k) for k in range(j, i + 1)], (df_name, i + 1), before, ) prevs.append(key) else: prevs = [None] for i in range(df.npartitions - 1): key = (name_a, i) dsk[key] = ( _tail_timedelta, [(df_name, i)], (df_name, i + 1), before, ) prevs.append(key) else: prevs = [None] * df.npartitions return dsk, prevs def _head_timedelta(current, next_, after): """Return rows of ``next_`` whose index is before the last observation in ``current`` + ``after``. Parameters ---------- current : DataFrame next_ : DataFrame after : timedelta Returns ------- overlapped : DataFrame """ return next_[next_.index < (current.index.max() + after)] def _tail_timedelta(prevs, current, before): """Return the concatenated rows of each dataframe in ``prevs`` whose index is after the first observation in ``current`` - ``before``. Parameters ---------- current : DataFrame prevs : list of DataFrame objects before : timedelta Returns ------- overlapped : DataFrame """ selected = methods.concat( [prev[prev.index > (current.index.min() - before)] for prev in prevs] ) return selected class Rolling: """Provides rolling window calculations.""" def __init__( self, obj, window=None, min_periods=None, center=False, win_type=None, axis=no_default, ): self.obj = obj # dataframe or series self.window = window self.min_periods = min_periods = center self.axis = axis self.win_type = win_type # Allow pandas to raise if appropriate obj._meta.rolling(**self._rolling_kwargs()) # Using .rolling(window='2s'), pandas will convert the # offset str to a window in nanoseconds. But pandas doesn't # accept the integer window with win_type='freq', so we store # that information here. # See self._win_type = None if isinstance(self.window, int) else "freq" if self.axis in ("index", 1, "rows"): warnings.warn( "Using axis=1 in Rolling has been deprecated and will be removed " "in a future version.", FutureWarning, ) def _rolling_kwargs(self): kwargs = { "window": self.window, "min_periods": self.min_periods, "center":, "win_type": self.win_type, } if self.axis is not no_default: kwargs["axis"] = self.axis return kwargs @property def _has_single_partition(self): """ Indicator for whether the object has a single partition (True) or multiple (False). """ return ( self.axis in (1, "columns") or (isinstance(self.window, Integral) and self.window <= 1) or self.obj.npartitions == 1 ) @staticmethod def pandas_rolling_method(df, rolling_kwargs, name, *args, **kwargs): with check_axis_keyword_deprecation(): rolling = df.rolling(**rolling_kwargs) return getattr(rolling, name)(*args, **kwargs) def _call_method(self, method_name, *args, **kwargs): rolling_kwargs = self._rolling_kwargs() meta = self.pandas_rolling_method( self.obj._meta_nonempty, rolling_kwargs, method_name, *args, **kwargs ) if self._has_single_partition: # There's no overlap just use map_partitions return self.obj.map_partitions( self.pandas_rolling_method, rolling_kwargs, method_name, *args, token=method_name, meta=meta, **kwargs, ) # Convert window to overlap if before = self.window // 2 after = self.window - before - 1 elif self._win_type == "freq": before = pd.Timedelta(self.window) after = 0 else: before = self.window - 1 after = 0 return map_overlap( self.pandas_rolling_method, self.obj, before, after, rolling_kwargs, method_name, *args, token=method_name, meta=meta, **kwargs, )
[docs] @derived_from(pd_Rolling) def count(self): return self._call_method("count")
@derived_from(pd_Rolling) def cov(self): return self._call_method("cov")
[docs] @derived_from(pd_Rolling) def sum(self): return self._call_method("sum")
[docs] @derived_from(pd_Rolling) def mean(self): return self._call_method("mean")
[docs] @derived_from(pd_Rolling) def median(self): return self._call_method("median")
[docs] @derived_from(pd_Rolling) def min(self): return self._call_method("min")
[docs] @derived_from(pd_Rolling) def max(self): return self._call_method("max")
[docs] @derived_from(pd_Rolling) def std(self, ddof=1): return self._call_method("std", ddof=1)
[docs] @derived_from(pd_Rolling) def var(self, ddof=1): return self._call_method("var", ddof=1)
[docs] @derived_from(pd_Rolling) def skew(self): return self._call_method("skew")
[docs] @derived_from(pd_Rolling) def kurt(self): return self._call_method("kurt")
[docs] @derived_from(pd_Rolling) def quantile(self, quantile): return self._call_method("quantile", quantile)
[docs] @derived_from(pd_Rolling) def apply( self, func, raw=False, engine="cython", engine_kwargs=None, args=None, kwargs=None, ): kwargs = kwargs or {} args = args or () return self._call_method( "apply", func, raw=raw, engine=engine, engine_kwargs=engine_kwargs, args=args, kwargs=kwargs, )
@derived_from(pd_Rolling) def aggregate(self, func, *args, **kwargs): return self._call_method("agg", func, *args, **kwargs) agg = aggregate def __repr__(self): def order(item): k, v = item _order = { "window": 0, "min_periods": 1, "center": 2, "win_type": 3, "axis": 4, } return _order[k] rolling_kwargs = self._rolling_kwargs() rolling_kwargs["window"] = self.window rolling_kwargs["win_type"] = self._win_type return "Rolling [{}]".format( ",".join( f"{k}={v}" for k, v in sorted(rolling_kwargs.items(), key=order) if v is not None ) ) class RollingGroupby(Rolling): def __init__( self, groupby, window=None, min_periods=None, center=False, win_type=None, axis=0, ): self._groupby_kwargs = groupby._groupby_kwargs self._groupby_slice = groupby._slice obj = groupby.obj if self._groupby_slice is not None: if isinstance(self._groupby_slice, str): sliced_plus = [self._groupby_slice] else: sliced_plus = list(self._groupby_slice) if isinstance(, str): sliced_plus.append( else: sliced_plus.extend( obj = obj[sliced_plus] super().__init__( obj, window=window, min_periods=min_periods, center=center, win_type=win_type, axis=axis, ) def _rolling_kwargs(self): kwargs = super()._rolling_kwargs() if kwargs.get("axis", None) in (0, "index"): # it's a default, no need to pass kwargs.pop("axis") return kwargs @staticmethod def pandas_rolling_method( df, rolling_kwargs, name, *args, groupby_kwargs=None, groupby_slice=None, **kwargs, ): groupby = df.groupby(**groupby_kwargs) if groupby_slice: groupby = groupby[groupby_slice] rolling = groupby.rolling(**rolling_kwargs) return getattr(rolling, name)(*args, **kwargs).sort_index(level=-1) def _call_method(self, method_name, *args, **kwargs): return super()._call_method( method_name, *args, groupby_kwargs=self._groupby_kwargs, groupby_slice=self._groupby_slice, **kwargs, )