from __future__ import annotations import operator import warnings from collections.abc import Callable, Iterator, Mapping, Sequence from functools import partial, wraps from numbers import Integral, Number from operator import getitem from pprint import pformat from typing import Any, ClassVar, Literal, cast import numpy as np import pandas as pd from pandas.api.types import ( is_bool_dtype, is_datetime64_any_dtype, is_extension_array_dtype, is_numeric_dtype, is_timedelta64_dtype, ) from tlz import first, merge, partition_all, remove, unique import dask.array as da from dask import config, core from dask.array.core import Array, normalize_arg from dask.bag import map_partitions as map_bag_partitions from dask.base import ( DaskMethodsMixin, dont_optimize, is_dask_collection, named_schedulers, tokenize, ) from dask.blockwise import Blockwise, BlockwiseDep, BlockwiseDepDict, blockwise from dask.context import globalmethod from dask.dataframe import methods from dask.dataframe._compat import ( PANDAS_GE_140, PANDAS_GE_150, PANDAS_GE_200, PANDAS_GE_210, PANDAS_GE_300, PANDAS_VERSION, check_convert_dtype_deprecation, check_nuisance_columns_warning, check_numeric_only_deprecation, check_reductions_runtime_warning, is_string_dtype, ) from dask.dataframe.accessor import CachedAccessor, DatetimeAccessor, StringAccessor from dask.dataframe.categorical import CategoricalAccessor, categorize from dask.dataframe.dispatch import ( get_parallel_type, group_split_dispatch, hash_object_dispatch, meta_nonempty, ) from dask.dataframe.optimize import optimize from dask.dataframe.utils import ( AttributeNotImplementedError, check_matching_columns, check_numeric_only_valid, clear_known_categories, drop_by_shallow_copy, get_numeric_only_kwargs, has_known_categories, index_summary, insert_meta_param_description, is_dataframe_like, is_index_like, is_series_like, make_meta, meta_frame_constructor, meta_series_constructor, pyarrow_strings_enabled, raise_on_meta_error, valid_divisions, ) from dask.delayed import Delayed, delayed, unpack_collections from dask.highlevelgraph import HighLevelGraph from dask.layers import DataFrameTreeReduction from dask.typing import Graph, NestedKeys, no_default from dask.utils import ( F, IndexCallable, M, OperatorMethodMixin, _deprecated, _deprecated_kwarg, apply, derived_from, funcname, get_meta_library, has_keyword, is_arraylike, iter_chunks, key_split, maybe_pluralize, memory_repr, parse_bytes, partial_by_order, pseudorandom, put_lines, random_state_data, typename, ) from dask.widgets import get_template DEFAULT_GET = named_schedulers.get("threads", named_schedulers["sync"]) GROUP_KEYS_DEFAULT: bool | None = True if PANDAS_GE_150 and not PANDAS_GE_200: GROUP_KEYS_DEFAULT = None pd.set_option("compute.use_numexpr", False) def _numeric_only(func): """Decorator for methods that accept a numeric_only kwarg""" @wraps(func) def wrapper(self, *args, **kwargs): # numeric_only is None by default - in that case self = self. if kwargs.get("numeric_only") is False: raise NotImplementedError( "'numeric_only=False' is not implemented in Dask." ) elif kwargs.get("numeric_only") is True: self = self._get_numeric_data() return func(self, *args, **kwargs) return wrapper def _numeric_data(func): """Modified version of the above decorator, right now only used with std. We don't need raising NotImplementedError there, because it's handled by _numeric_only_maybe_warn instead. This is a temporary solution that needs more time to be generalized.""" @wraps(func) def wrapper(self, *args, **kwargs): if kwargs.get("numeric_only") is True: self = self._get_numeric_data() return func(self, *args, **kwargs) return wrapper def _numeric_only_maybe_warn(df, numeric_only, default=None): """Update numeric_only to get rid of no_default, and possibly warn about default value. TODO: should move to numeric_only decorator. See https://github.com/dask/dask/pull/9952 """ if is_dataframe_like(df): warn_numeric_only = False if numeric_only is no_default: if PANDAS_GE_200: numeric_only = False else: warn_numeric_only = True numerics = df._meta._get_numeric_data() has_non_numerics = len(numerics.columns) < len(df._meta.columns) if has_non_numerics: if numeric_only is False: raise NotImplementedError( "'numeric_only=False' is not implemented in Dask." ) elif warn_numeric_only: warnings.warn( "The default value of numeric_only in dask will be changed to False in " "the future when using dask with pandas 2.0", FutureWarning, ) if numeric_only is no_default and default is not None: numeric_only = default return {} if numeric_only is no_default else {"numeric_only": numeric_only} def _concat(args, ignore_index=False): if not args: return args if isinstance(first(core.flatten(args)), np.ndarray): return da.core.concatenate3(args) if not has_parallel_type(args[0]): try: return pd.Series(args) except Exception: return args # We filter out empty partitions here because pandas frequently has # inconsistent dtypes in results between empty and non-empty frames. # Ideally this would be handled locally for each operation, but in practice # this seems easier. TODO: don't do this. args2 = [i for i in args if len(i)] return ( args[0] if not args2 else methods.concat(args2, uniform=True, ignore_index=ignore_index) ) def _determine_split_out_shuffle(shuffle_method, split_out): """Determine the default shuffle behavior based on split_out""" if shuffle_method is None: if split_out > 1: # FIXME: This is using a different default but it is not fully # understood why this is a better choice. # For more context, see # https://github.com/dask/dask/pull/9826/files#r1072395307 # https://github.com/dask/distributed/issues/5502 return config.get("dataframe.shuffle.method", None) or "tasks" else: return False if shuffle_method is True: return config.get("dataframe.shuffle.method", None) or "tasks" return shuffle_method def _dummy_numpy_dispatcher( *arg_names: Literal["dtype", "out"], deprecated: bool = False ) -> Callable[[F], F]: """Decorator to handle the out= and dtype= keyword arguments. These parameters are deprecated in all dask.dataframe reduction methods and will be soon completely disallowed. However, these methods must continue accepting 'out=None' and/or 'dtype=None' indefinitely in order to support numpy dispatchers. For example, ``np.mean(df)`` calls ``df.mean(out=None, dtype=None)``. Parameters ---------- deprecated: bool If True, warn if not None and then pass the parameter to the wrapped function If False, raise error if not None; do not pass the parameter down. See Also -------- _deprecated_kwarg """ def decorator(func: F) -> F: @wraps(func) def wrapper(*args, **kwargs): for name in arg_names: if deprecated: if kwargs.get(name, None) is not None: warnings.warn( f"the '{name}' keyword is deprecated and " "will be removed in a future version.", FutureWarning, stacklevel=2, ) else: if kwargs.pop(name, None) is not None: raise ValueError(f"the '{name}' keyword is not supported") return func(*args, **kwargs) return cast(F, wrapper) return decorator def finalize(results): return _concat(results) class Scalar(DaskMethodsMixin, OperatorMethodMixin): """A Dask object to represent a pandas scalar""" def __init__(self, dsk, name, meta, divisions=None): # divisions is ignored, only present to be compatible with other # objects. if not isinstance(dsk, HighLevelGraph): dsk = HighLevelGraph.from_collections(name, dsk, dependencies=[]) self.dask = dsk self._name = name self._parent_meta = pd.Series(dtype="float64") meta = make_meta(meta, parent_meta=self._parent_meta) if is_dataframe_like(meta) or is_series_like(meta) or is_index_like(meta): raise TypeError( f"Expected meta to specify scalar, got {typename(type(meta))}" ) self._meta = meta def __dask_graph__(self) -> Graph: return self.dask def __dask_keys__(self) -> NestedKeys: return [self.key] def __dask_tokenize__(self): return self._name def __dask_layers__(self) -> Sequence[str]: return (self._name,) __dask_optimize__ = globalmethod( optimize, key="dataframe_optimize", falsey=dont_optimize ) __dask_scheduler__ = staticmethod(DEFAULT_GET) def __dask_postcompute__(self): return first, () def __dask_postpersist__(self): return self._rebuild, () def _rebuild(self, dsk, *, rename=None): name = self._name if rename: name = rename.get(name, name) return Scalar(dsk, name, self._meta, self.divisions) @property def _meta_nonempty(self): return self._meta @property def dtype(self): return self._meta.dtype def __dir__(self): o = set(dir(type(self))) o.update(self.__dict__) if not hasattr(self._meta, "dtype"): o.remove("dtype") # dtype only in `dir` if available return list(o) @property def divisions(self): """Dummy divisions to be compat with Series and DataFrame""" return (None, None) def __repr__(self): name = self._name if len(self._name) < 10 else self._name[:7] + "..." if hasattr(self._meta, "dtype"): extra = ", dtype=%s" % self._meta.dtype else: extra = ", type=%s" % type(self._meta).__name__ return f"dd.Scalar<{name}{extra}>" def __array__(self): # array interface is required to support pandas instance + Scalar # Otherwise, above op results in pd.Series of Scalar (object dtype) return np.asarray(self.compute()) @property def _args(self): return (self.dask, self._name, self._meta) def __getstate__(self): return self._args def __setstate__(self, state): self.dask, self._name, self._meta = state def __bool__(self): raise TypeError( f"Trying to convert {self} to a boolean value. Because Dask objects are " "lazily evaluated, they cannot be converted to a boolean value or used " "in boolean conditions like if statements. Try calling .compute() to " "force computation prior to converting to a boolean value or using in " "a conditional statement." ) @property def key(self): return (self._name, 0) @classmethod def _get_unary_operator(cls, op): def f(self): name = funcname(op) + "-" + tokenize(self) dsk = {(name, 0): (op, (self._name, 0))} meta = op(self._meta_nonempty) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return Scalar(graph, name, meta) return f @classmethod def _get_binary_operator(cls, op, inv=False): return lambda self, other: _scalar_binary(op, self, other, inv=inv) def to_delayed(self, optimize_graph=True): """Convert into a ``dask.delayed`` object. Parameters ---------- optimize_graph : bool, optional If True [default], the graph is optimized before converting into ``dask.delayed`` objects. """ dsk = self.__dask_graph__() layer = self.__dask_layers__()[0] if optimize_graph: dsk = self.__dask_optimize__(dsk, self.__dask_keys__()) layer = "delayed-" + self._name dsk = HighLevelGraph.from_collections(layer, dsk, dependencies=()) return Delayed(self.key, dsk, layer=layer) def _scalar_binary(op, self, other, inv=False): name = f"{funcname(op)}-{tokenize(self, other)}" dependencies = [self] dsk = {} return_type = get_parallel_type(other) if isinstance(other, Scalar): dependencies.append(other) other_key = (other._name, 0) elif is_dask_collection(other): return NotImplemented else: other_key = other dsk[(name, 0)] = ( (op, other_key, (self._name, 0)) if inv else (op, (self._name, 0), other_key) ) other_meta = make_meta(other, parent_meta=self._parent_meta) other_meta_nonempty = meta_nonempty(other_meta) if inv: meta = op(other_meta_nonempty, self._meta_nonempty) else: meta = op(self._meta_nonempty, other_meta_nonempty) graph = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies) if return_type is not Scalar: return return_type(graph, name, meta, [other.index.min(), other.index.max()]) else: return Scalar(graph, name, meta) class _Frame(DaskMethodsMixin, OperatorMethodMixin): """Superclass for DataFrame and Series Parameters ---------- dsk: dict The dask graph to compute this DataFrame name: str The key prefix that specifies which keys in the dask comprise this particular DataFrame / Series meta: pandas.DataFrame, pandas.Series, or pandas.Index An empty pandas object with names, dtypes, and indices matching the expected output. divisions: tuple of index values Values along which we partition our blocks on the index """ def __init__(self, dsk, name, meta, divisions): if not isinstance(dsk, HighLevelGraph): dsk = HighLevelGraph.from_collections(name, dsk, dependencies=[]) self.dask = dsk self._name = name meta = make_meta(meta) if not self._is_partition_type(meta): raise TypeError( f"Expected meta to specify type {type(self).__name__}, got type " f"{typename(type(meta))}" ) self._meta = meta self.divisions = tuple(divisions) # Optionally cast object dtypes to `pyarrow` strings. # By default, if `pyarrow` and `pandas>=2` are installed, # we convert to pyarrow strings. # Disable for read_parquet since the reader takes care of this # conversion if pyarrow_strings_enabled() and "read-parquet" not in name: from dask.dataframe._pyarrow import check_pyarrow_string_supported check_pyarrow_string_supported() from dask.dataframe._pyarrow import ( is_object_string_dataframe, is_object_string_dtype, is_object_string_index, is_object_string_series, to_pyarrow_string, ) if ( is_object_string_dataframe(meta) or is_object_string_series(meta) or is_object_string_index(meta) ): # Prior to pandas=1.4, `pd.Index` couldn't contain extension dtypes. # Here we don't cast objects to pyarrow strings where only the index # contains non-pyarrow string data. if not PANDAS_GE_140 and ( ( is_object_string_dataframe(meta) and not any(is_object_string_dtype(d) for d in meta.dtypes) ) or ( is_object_string_series(meta) and not is_object_string_dtype(meta.dtype) ) or is_object_string_index(meta) ): return # this is an internal call, and if we enforce metadata, # it may interfere when reading csv with enforce=False result = self.map_partitions( to_pyarrow_string, enforce_metadata=False, token="to_pyarrow_string" ) self.dask = result.dask self._name = result._name self._meta = result._meta self.divisions = result.divisions def __dask_graph__(self) -> Graph: return self.dask def __dask_keys__(self) -> NestedKeys: return [(self._name, i) for i in range(self.npartitions)] def __dask_layers__(self) -> Sequence[str]: return (self._name,) def __dask_tokenize__(self): return self._name __dask_optimize__ = globalmethod( optimize, key="dataframe_optimize", falsey=dont_optimize ) __dask_scheduler__ = staticmethod(DEFAULT_GET) def __dask_postcompute__(self): return finalize, () def __dask_postpersist__(self): return self._rebuild, () def _rebuild(self, dsk, *, rename=None): name = self._name if rename: name = rename.get(name, name) return type(self)(dsk, name, self._meta, self.divisions) @property def _constructor(self): return new_dd_object @property def divisions(self): """ Tuple of ``npartitions + 1`` values, in ascending order, marking the lower/upper bounds of each partition's index. Divisions allow Dask to know which partition will contain a given value, significantly speeding up operations like `loc`, `merge`, and `groupby` by not having to search the full dataset. Example: for ``divisions = (0, 10, 50, 100)``, there are three partitions, where the index in each partition contains values [0, 10), [10, 50), and [50, 100], respectively. Dask therefore knows ``df.loc[45]`` will be in the second partition. When every item in ``divisions`` is ``None``, the divisions are unknown. Most operations can still be performed, but some will be much slower, and a few may fail. It is uncommon to set ``divisions`` directly. Instead, use ``set_index``, which sorts and splits the data as needed. See https://docs.dask.org/en/latest/dataframe-design.html#partitions. """ return self._divisions @divisions.setter def divisions(self, value): if not isinstance(value, tuple): raise TypeError("divisions must be a tuple") if hasattr(self, "_divisions") and len(value) != len(self._divisions): n = len(self._divisions) raise ValueError( f"This dataframe has npartitions={n - 1}, divisions should be a " f"tuple of length={n}, got {len(value)}" ) if None in value: if any(v is not None for v in value): raise ValueError( "divisions may not contain a mix of None and non-None values" ) else: # Known divisions, check monotonically increasing # XXX: if the index dtype is an ordered categorical dtype, then we skip the # sortedness check, since the order is dtype dependent index_dtype = getattr(self._meta, "index", self._meta).dtype if not ( isinstance(index_dtype, pd.CategoricalDtype) and index_dtype.ordered ): if value != tuple(sorted(value)): raise ValueError("divisions must be sorted") self._divisions = value @property def npartitions(self) -> int: """Return number of partitions""" return len(self.divisions) - 1 @property @derived_from(pd.DataFrame) def attrs(self): return self._meta.attrs @attrs.setter def attrs(self, value): self._meta.attrs = dict(value) @property def size(self): """Size of the Series or DataFrame as a Delayed object. Examples -------- >>> series.size # doctest: +SKIP dd.Scalar """ return self.reduction( methods.size, np.sum, token="size", meta=int, split_every=False ) @property def _meta_nonempty(self): """A non-empty version of `_meta` with fake data.""" return meta_nonempty(self._meta) @property def _args(self): return (self.dask, self._name, self._meta, self.divisions) def __getstate__(self): return self._args def __setstate__(self, state): self.dask, self._name, self._meta, self._divisions = state def copy(self, deep=False): """Make a copy of the dataframe This is strictly a shallow copy of the underlying computational graph. It does not affect the underlying data Parameters ---------- deep : boolean, default False The deep value must be `False` and it is declared as a parameter just for compatibility with third-party libraries like cuDF """ if deep is not False: raise ValueError( "The `deep` value must be False. This is strictly a shallow copy " "of the underlying computational graph." ) return new_dd_object(self.dask, self._name, self._meta, self.divisions) def __array__(self, dtype=None, **kwargs): self._computed = self.compute() x = np.array(self._computed) return x def __array_wrap__(self, array, context=None): raise NotImplementedError def __array_ufunc__(self, numpy_ufunc, method, *inputs, **kwargs): out = kwargs.get("out", ()) for x in inputs + out: # ufuncs work with 0-dimensional NumPy ndarrays # so we don't want to raise NotImplemented if isinstance(x, np.ndarray) and x.shape == (): continue elif not isinstance( x, (Number, Scalar, _Frame, Array, pd.DataFrame, pd.Series, pd.Index) ): return NotImplemented if method == "__call__": if numpy_ufunc.signature is not None: return NotImplemented if numpy_ufunc.nout > 1: # ufuncs with multiple output values # are not yet supported for frames return NotImplemented else: return elemwise(numpy_ufunc, *inputs, **kwargs) else: # ufunc methods are not yet supported for frames return NotImplemented @property def _elemwise(self): return elemwise def _repr_data(self): raise NotImplementedError @property def _repr_divisions(self): name = f"npartitions={self.npartitions}" if self.known_divisions: divisions = pd.Index(self.divisions, name=name) else: # avoid to be converted to NaN divisions = pd.Index([""] * (self.npartitions + 1), name=name) return divisions def __repr__(self): data = self._repr_data().to_string(max_rows=5, show_dimensions=False) _str_fmt = """Dask {klass} Structure: {data} Dask Name: {name}, {layers}""" if len(self.columns) == 0: data = data.partition("\n")[-1].replace("Index", "Divisions") _str_fmt = f"Empty {_str_fmt}" return _str_fmt.format( klass=self.__class__.__name__, data=data, name=key_split(self._name), layers=maybe_pluralize(len(self.dask.layers), "graph layer"), ) @property def index(self): """Return dask Index instance""" return self.map_partitions( getattr, "index", token=key_split(self._name) + "-index", meta=self._meta.index, enforce_metadata=False, ) @index.setter def index(self, value): self.divisions = value.divisions result = map_partitions( methods.assign_index, self, value, enforce_metadata=False ) self.dask = result.dask self._name = result._name self._meta = result._meta def reset_index(self, drop=False): """Reset the index to the default index. Note that unlike in ``pandas``, the reset ``dask.dataframe`` index will not be monotonically increasing from 0. Instead, it will restart at 0 for each partition (e.g. ``index1 = [0, ..., 10], index2 = [0, ...]``). This is due to the inability to statically know the full length of the index. For DataFrame with multi-level index, returns a new DataFrame with labeling information in the columns under the index names, defaulting to 'level_0', 'level_1', etc. if any are None. For a standard index, the index name will be used (if set), otherwise a default 'index' or 'level_0' (if 'index' is already taken) will be used. Parameters ---------- drop : boolean, default False Do not try to insert index into dataframe columns. """ return self.map_partitions( M.reset_index, drop=drop, enforce_metadata=False ).clear_divisions() @property def known_divisions(self): """Whether divisions are already known""" return len(self.divisions) > 0 and self.divisions[0] is not None def clear_divisions(self): """Forget division information""" divisions = (None,) * (self.npartitions + 1) return type(self)(self.dask, self._name, self._meta, divisions) def enforce_runtime_divisions(self): """Enforce the current divisions at runtime""" if not self.known_divisions: raise ValueError("No known divisions to enforce!") def _check_divisions(df, expect): # Check divisions id, expect_min, expect_max, last = expect real_min = df.index.min() real_max = df.index.max() # Upper division of the last partition is often set to # the max value. For all other partitions, the upper # division should be greater than the maximum value. valid_min = real_min >= expect_min valid_max = (real_max <= expect_max) if last else (real_max < expect_max) if not (valid_min and valid_max): raise RuntimeError( f"`enforce_runtime_divisions` failed for partition {id}." f" Expected a range of [{expect_min}, {expect_max}), " f" but the real range was [{real_min}, {real_max}]." ) return df return self.map_partitions( _check_divisions, BlockwiseDepDict( { (i,): (i, dmin, dmax, i == (self.npartitions - 1)) for i, (dmin, dmax) in enumerate( zip(self.divisions[:-1], self.divisions[1:]) ) } ), meta=self._meta, enforce_metadata=False, ) def compute_current_divisions(self, col=None): """Compute the current divisions of the DataFrame. This method triggers immediate computation. If you find yourself running this command repeatedly for the same dataframe, we recommend storing the result so you don't have to rerun it. If the column or index values overlap between partitions, raises ``ValueError``. To prevent this, make sure the data are sorted by the column or index. Parameters ---------- col : string, optional Calculate the divisions for a non-index column by passing in the name of the column. If col is not specified, the index will be used to calculate divisions. In this case, if the divisions are already known, they will be returned immediately without computing. Examples -------- >>> import dask >>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1h").clear_divisions() >>> divisions = ddf.compute_current_divisions() >>> print(divisions) # doctest: +NORMALIZE_WHITESPACE (Timestamp('2021-01-01 00:00:00'), Timestamp('2021-01-02 00:00:00'), Timestamp('2021-01-03 00:00:00'), Timestamp('2021-01-04 00:00:00'), Timestamp('2021-01-05 00:00:00'), Timestamp('2021-01-06 00:00:00'), Timestamp('2021-01-06 23:00:00')) >>> ddf.divisions = divisions >>> ddf.known_divisions True >>> ddf = ddf.reset_index().clear_divisions() >>> divisions = ddf.compute_current_divisions("timestamp") >>> print(divisions) # doctest: +NORMALIZE_WHITESPACE (Timestamp('2021-01-01 00:00:00'), Timestamp('2021-01-02 00:00:00'), Timestamp('2021-01-03 00:00:00'), Timestamp('2021-01-04 00:00:00'), Timestamp('2021-01-05 00:00:00'), Timestamp('2021-01-06 00:00:00'), Timestamp('2021-01-06 23:00:00')) >>> ddf = ddf.set_index("timestamp", divisions=divisions, sorted=True) """ if col is None and self.known_divisions: return self.divisions from dask.dataframe.shuffle import compute_divisions return compute_divisions(self, col=col) def get_partition(self, n): """ Get a dask DataFrame/Series representing the `nth` partition. Parameters ---------- n : int The 0-indexed partition number to select. Returns ------- Dask DataFrame or Series The same type as the original object. Examples -------- >>> import dask >>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1h") >>> ddf.get_partition(0) # doctest: +NORMALIZE_WHITESPACE Dask DataFrame Structure: name id x y npartitions=1 2021-01-01 string int64 float64 float64 2021-01-02 ... ... ... ... Dask Name: get-partition, 3 graph layers See Also -------- DataFrame.partitions """ if 0 <= n < self.npartitions: name = f"get-partition-{str(n)}-{self._name}" divisions = self.divisions[n : n + 2] layer = {(name, 0): (self._name, n)} graph = HighLevelGraph.from_collections(name, layer, dependencies=[self]) return new_dd_object(graph, name, self._meta, divisions) else: msg = f"n must be 0 <= n < {self.npartitions}" raise ValueError(msg) def _drop_duplicates_shuffle( self, split_out, split_every, shuffle_method, ignore_index, **kwargs ): # Private method that drops duplicate rows using a # shuffle-based algorithm. # Used by `_Frame.drop_duplicates`. # Make sure we have a DataFrame to shuffle if isinstance(self, Index): df = self.to_frame(name=self.name or "__index__") elif isinstance(self, Series): df = self.to_frame(name=self.name or "__series__") else: df = self # Choose appropriate shuffle partitioning split_every = 8 if split_every is None else split_every shuffle_npartitions = max( df.npartitions // (split_every or df.npartitions), split_out, ) # Deduplicate, then shuffle, then deduplicate again chunk = M.drop_duplicates deduplicated = ( df.map_partitions( chunk, token="drop-duplicates-chunk", meta=df._meta, ignore_index=ignore_index, enforce_metadata=False, transform_divisions=False, **kwargs, ) .shuffle( kwargs.get("subset", None) or list(df.columns), ignore_index=ignore_index, npartitions=shuffle_npartitions, shuffle_method=shuffle_method, ) .map_partitions( chunk, meta=df._meta, ignore_index=ignore_index, token="drop-duplicates-agg", transform_divisions=False, **kwargs, ) ) # Convert back to Series/Index if necessary if isinstance(self, Index): deduplicated = deduplicated.set_index( self.name or "__index__", sort=False ).index if deduplicated.name == "__index__": deduplicated.name = None elif isinstance(self, Series): deduplicated = deduplicated[self.name or "__series__"] if deduplicated.name == "__series__": deduplicated.name = None # Return `split_out` partitions return deduplicated.repartition(npartitions=split_out) @_deprecated_kwarg("shuffle", "shuffle_method") @derived_from( pd.DataFrame, inconsistencies="keep=False will raise a ``NotImplementedError``", ) def drop_duplicates( self, subset=None, split_every=None, split_out=1, shuffle_method=None, ignore_index=False, **kwargs, ): if subset is not None: # Let pandas error on bad inputs self._meta_nonempty.drop_duplicates(subset=subset, **kwargs) kwargs["subset"] = subset split_out_setup = split_out_on_cols split_out_setup_kwargs = {"cols": subset} else: self._meta_nonempty.drop_duplicates(**kwargs) split_out_setup = split_out_setup_kwargs = None if kwargs.get("keep", True) is False: raise NotImplementedError("drop_duplicates with keep=False") # Check if we should use a shuffle-based algorithm, # which is typically faster when we are not reducing # to a small number of partitions shuffle_method = _determine_split_out_shuffle(shuffle_method, split_out) if shuffle_method: return self._drop_duplicates_shuffle( split_out, split_every, shuffle_method, ignore_index, **kwargs, ) # Use general ACA reduction # (Usually best when split_out == 1) chunk = M.drop_duplicates return aca( self, chunk=chunk, aggregate=chunk, meta=self._meta, token="drop-duplicates", split_every=split_every, split_out=split_out, split_out_setup=split_out_setup, split_out_setup_kwargs=split_out_setup_kwargs, ignore_index=ignore_index, **kwargs, ) def __len__(self): return self.reduction( len, np.sum, token="len", meta=int, split_every=False ).compute() def __bool__(self): raise ValueError( f"The truth value of a {self.__class__.__name__} is ambiguous. " "Use a.any() or a.all()." ) __nonzero__ = __bool__ # python 2 def _scalarfunc(self, cast_type): def wrapper(): raise TypeError(f"cannot convert the series to {cast_type}") return wrapper def __float__(self): return self._scalarfunc(float) def __int__(self): return self._scalarfunc(int) __long__ = __int__ # python 2 def __complex__(self): return self._scalarfunc(complex) @insert_meta_param_description(pad=12) def map_partitions(self, func, *args, **kwargs): """Apply Python function on each DataFrame partition. Note that the index and divisions are assumed to remain unchanged. 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. 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. 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 Examples -------- Given a DataFrame, Series, or Index, such as: >>> import pandas as pd >>> import dask.dataframe as dd >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5], ... 'y': [1., 2., 3., 4., 5.]}) >>> ddf = dd.from_pandas(df, npartitions=2) One can use ``map_partitions`` to apply a function on each partition. Extra arguments and keywords can optionally be provided, and will be passed to the function after the partition. Here we apply a function with arguments and keywords to a DataFrame, resulting in a Series: >>> def myadd(df, a, b=1): ... return df.x + df.y + a + b >>> res = ddf.map_partitions(myadd, 1, b=2) >>> res.dtype dtype('float64') Here we apply a function to a Series resulting in a Series: >>> res = ddf.x.map_partitions(lambda x: len(x)) # ddf.x is a Dask Series Structure >>> res.dtype dtype('int64') By default, dask tries to infer the output metadata by running your provided function on some fake data. This works well in many cases, but can sometimes be expensive, or even fail. To avoid this, you can manually specify the output metadata with the ``meta`` keyword. This can be specified in many forms, for more information see ``dask.dataframe.utils.make_meta``. Here we specify the output is a Series with no name, and dtype ``float64``: >>> res = ddf.map_partitions(myadd, 1, b=2, meta=(None, 'f8')) Here we map a function that takes in a DataFrame, and returns a DataFrame with a new column: >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y)) >>> res.dtypes x int64 y float64 z float64 dtype: object As before, the output metadata can also be specified manually. This time we pass in a ``dict``, as the output is a DataFrame: >>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y), ... meta={'x': 'i8', 'y': 'f8', 'z': 'f8'}) In the case where the metadata doesn't change, you can also pass in the object itself directly: >>> res = ddf.map_partitions(lambda df: df.head(), meta=ddf) Also note that the index and divisions are assumed to remain unchanged. If the function you're mapping changes the index/divisions, you'll need to clear them afterwards: >>> ddf.map_partitions(func).clear_divisions() # doctest: +SKIP Your map function gets information about where it is in the dataframe by accepting a special ``partition_info`` keyword argument. >>> def func(partition, partition_info=None): ... pass This will receive the following information: >>> partition_info # doctest: +SKIP {'number': 1, 'division': 3} For each argument and keyword arguments that are dask dataframes you will receive the number (n) which represents the nth partition of the dataframe and the division (the first index value in the partition). If divisions are not known (for instance if the index is not sorted) then you will get None as the division. """ return map_partitions(func, self, *args, **kwargs) @insert_meta_param_description(pad=12) def map_overlap(self, func, before, after, *args, **kwargs): """Apply a function to each partition, sharing rows with adjacent partitions. This can be useful for implementing windowing functions such as ``df.rolling(...).mean()`` or ``df.diff()``. Parameters ---------- func : function Function applied to each partition. 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``. 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. 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 Notes ----- Given positive integers ``before`` and ``after``, and a function ``func``, ``map_overlap`` does the following: 1. Prepend ``before`` rows to each partition ``i`` from the end of partition ``i - 1``. The first partition has no rows prepended. 2. Append ``after`` rows to each partition ``i`` from the beginning of partition ``i + 1``. The last partition has no rows appended. 3. Apply ``func`` to each partition, passing in any extra ``args`` and ``kwargs`` if provided. 4. Trim ``before`` rows from the beginning of all but the first partition. 5. Trim ``after`` rows from the end of all but the last partition. Examples -------- Given a DataFrame, Series, or Index, such as: >>> import pandas as pd >>> import dask.dataframe as dd >>> df = pd.DataFrame({'x': [1, 2, 4, 7, 11], ... 'y': [1., 2., 3., 4., 5.]}) >>> ddf = dd.from_pandas(df, npartitions=2) A rolling sum with a trailing moving window of size 2 can be computed by overlapping 2 rows before each partition, and then mapping calls to ``df.rolling(2).sum()``: >>> ddf.compute() x y 0 1 1.0 1 2 2.0 2 4 3.0 3 7 4.0 4 11 5.0 >>> ddf.map_overlap(lambda df: df.rolling(2).sum(), 2, 0).compute() x y 0 NaN NaN 1 3.0 3.0 2 6.0 5.0 3 11.0 7.0 4 18.0 9.0 The pandas ``diff`` method computes a discrete difference shifted by a number of periods (can be positive or negative). This can be implemented by mapping calls to ``df.diff`` to each partition after prepending/appending that many rows, depending on sign: >>> def diff(df, periods=1): ... before, after = (periods, 0) if periods > 0 else (0, -periods) ... return df.map_overlap(lambda df, periods=1: df.diff(periods), ... periods, 0, periods=periods) >>> diff(ddf, 1).compute() x y 0 NaN NaN 1 1.0 1.0 2 2.0 1.0 3 3.0 1.0 4 4.0 1.0 If you have a ``DatetimeIndex``, you can use a ``pd.Timedelta`` for time- based windows or any ``pd.Timedelta`` convertible string: >>> ts = pd.Series(range(10), index=pd.date_range('2017', periods=10)) >>> dts = dd.from_pandas(ts, npartitions=2) >>> dts.map_overlap(lambda df: df.rolling('2D').sum(), ... pd.Timedelta('2D'), 0).compute() 2017-01-01 0.0 2017-01-02 1.0 2017-01-03 3.0 2017-01-04 5.0 2017-01-05 7.0 2017-01-06 9.0 2017-01-07 11.0 2017-01-08 13.0 2017-01-09 15.0 2017-01-10 17.0 Freq: D, dtype: float64 """ from dask.dataframe.rolling import map_overlap return map_overlap(func, self, before, after, *args, **kwargs) def memory_usage_per_partition(self, index=True, deep=False): """Return the memory usage of each partition Parameters ---------- index : bool, default True Specifies whether to include the memory usage of the index in returned Series. deep : bool, default False If True, introspect the data deeply by interrogating ``object`` dtypes for system-level memory consumption, and include it in the returned values. Returns ------- Series A Series whose index is the partition number and whose values are the memory usage of each partition in bytes. """ return self.map_partitions( total_mem_usage, index=index, deep=deep ).clear_divisions() @insert_meta_param_description(pad=12) def reduction( self, chunk, aggregate=None, combine=None, meta=no_default, token=None, split_every=None, chunk_kwargs=None, aggregate_kwargs=None, combine_kwargs=None, **kwargs, ): """Generic row-wise reductions. Parameters ---------- chunk : callable Function to operate on each partition. Should return a ``pandas.DataFrame``, ``pandas.Series``, or a scalar. aggregate : callable, optional Function to operate on the concatenated result of ``chunk``. If not specified, defaults to ``chunk``. Used to do the final aggregation in a tree reduction. The input to ``aggregate`` depends on the output of ``chunk``. If the output of ``chunk`` is a: - scalar: Input is a Series, with one row per partition. - Series: Input is a DataFrame, with one row per partition. Columns are the rows in the output series. - DataFrame: Input is a DataFrame, with one row per partition. Columns are the columns in the output dataframes. Should return a ``pandas.DataFrame``, ``pandas.Series``, or a scalar. combine : callable, optional Function to operate on intermediate concatenated results of ``chunk`` in a tree-reduction. If not provided, defaults to ``aggregate``. The input/output requirements should match that of ``aggregate`` described above. $META token : str, optional The name to use for the output keys. split_every : int, optional Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used, and all intermediates will be concatenated and passed to ``aggregate``. Default is 8. chunk_kwargs : dict, optional Keyword arguments to pass on to ``chunk`` only. aggregate_kwargs : dict, optional Keyword arguments to pass on to ``aggregate`` only. combine_kwargs : dict, optional Keyword arguments to pass on to ``combine`` only. kwargs : All remaining keywords will be passed to ``chunk``, ``combine``, and ``aggregate``. Examples -------- >>> import pandas as pd >>> import dask.dataframe as dd >>> df = pd.DataFrame({'x': range(50), 'y': range(50, 100)}) >>> ddf = dd.from_pandas(df, npartitions=4) Count the number of rows in a DataFrame. To do this, count the number of rows in each partition, then sum the results: >>> res = ddf.reduction(lambda x: x.count(), ... aggregate=lambda x: x.sum()) >>> res.compute() x 50 y 50 dtype: int64 Count the number of rows in a Series with elements greater than or equal to a value (provided via a keyword). >>> def count_greater(x, value=0): ... return (x >= value).sum() >>> res = ddf.x.reduction(count_greater, aggregate=lambda x: x.sum(), ... chunk_kwargs={'value': 25}) >>> res.compute() 25 Aggregate both the sum and count of a Series at the same time: >>> def sum_and_count(x): ... return pd.Series({'count': x.count(), 'sum': x.sum()}, ... index=['count', 'sum']) >>> res = ddf.x.reduction(sum_and_count, aggregate=lambda x: x.sum()) >>> res.compute() count 50 sum 1225 dtype: int64 Doing the same, but for a DataFrame. Here ``chunk`` returns a DataFrame, meaning the input to ``aggregate`` is a DataFrame with an index with non-unique entries for both 'x' and 'y'. We groupby the index, and sum each group to get the final result. >>> def sum_and_count(x): ... return pd.DataFrame({'count': x.count(), 'sum': x.sum()}, ... columns=['count', 'sum']) >>> res = ddf.reduction(sum_and_count, ... aggregate=lambda x: x.groupby(level=0).sum()) >>> res.compute() count sum x 50 1225 y 50 3725 """ if aggregate is None: aggregate = chunk if combine is None: if combine_kwargs: raise ValueError("`combine_kwargs` provided with no `combine`") combine = aggregate combine_kwargs = aggregate_kwargs chunk_kwargs = chunk_kwargs.copy() if chunk_kwargs else {} chunk_kwargs["aca_chunk"] = chunk combine_kwargs = combine_kwargs.copy() if combine_kwargs else {} combine_kwargs["aca_combine"] = combine aggregate_kwargs = aggregate_kwargs.copy() if aggregate_kwargs else {} aggregate_kwargs["aca_aggregate"] = aggregate return aca( self, chunk=_reduction_chunk, aggregate=_reduction_aggregate, combine=_reduction_combine, meta=meta, token=token, split_every=split_every, chunk_kwargs=chunk_kwargs, aggregate_kwargs=aggregate_kwargs, combine_kwargs=combine_kwargs, **kwargs, ) @derived_from(pd.DataFrame) def pipe(self, func, *args, **kwargs): # Taken from pandas: # https://github.com/pydata/pandas/blob/master/pandas/core/generic.py#L2698-L2707 if isinstance(func, tuple): func, target = func if target in kwargs: raise ValueError( "%s is both the pipe target and a keyword argument" % target ) kwargs[target] = self return func(*args, **kwargs) else: return func(self, *args, **kwargs) def random_split(self, frac, random_state=None, shuffle=False): """Pseudorandomly split dataframe into different pieces row-wise Parameters ---------- frac : list List of floats that should sum to one. random_state : int or np.random.RandomState If int create a new RandomState with this as the seed. Otherwise draw from the passed RandomState. shuffle : bool, default False If set to True, the dataframe is shuffled (within partition) before the split. Examples -------- 50/50 split >>> a, b = df.random_split([0.5, 0.5]) # doctest: +SKIP 80/10/10 split, consistent random_state >>> a, b, c = df.random_split([0.8, 0.1, 0.1], random_state=123) # doctest: +SKIP See Also -------- dask.DataFrame.sample """ if not np.allclose(sum(frac), 1): raise ValueError("frac should sum to 1") state_data = random_state_data(self.npartitions, random_state) token = tokenize(self, frac, random_state) name = "split-" + token layer = { (name, i): (pd_split, (self._name, i), frac, state, shuffle) for i, state in enumerate(state_data) } out = [] for i in range(len(frac)): name2 = "split-%d-%s" % (i, token) dsk2 = { (name2, j): (getitem, (name, j), i) for j in range(self.npartitions) } graph = HighLevelGraph.from_collections( name2, merge(dsk2, layer), dependencies=[self] ) out_df = type(self)(graph, name2, self._meta, self.divisions) out.append(out_df) return out def head(self, n=5, npartitions=1, compute=True): """First n rows of the dataset Parameters ---------- n : int, optional The number of rows to return. Default is 5. npartitions : int, optional Elements are only taken from the first ``npartitions``, with a default of 1. If there are fewer than ``n`` rows in the first ``npartitions`` a warning will be raised and any found rows returned. Pass -1 to use all partitions. compute : bool, optional Whether to compute the result, default is True. """ if npartitions <= -1: npartitions = self.npartitions # No need to warn if we're already looking at all partitions safe = npartitions != self.npartitions return self._head(n=n, npartitions=npartitions, compute=compute, safe=safe) def _head(self, n, npartitions, compute, safe): if npartitions <= -1: npartitions = self.npartitions if npartitions > self.npartitions: raise ValueError( f"only {self.npartitions} partitions, head received {npartitions}" ) name = f"head-{npartitions}-{n}-{self._name}" if safe: head = safe_head else: head = M.head if npartitions > 1: name_p = f"head-partial-{n}-{self._name}" dsk = {} for i in range(npartitions): dsk[(name_p, i)] = (M.head, (self._name, i), n) concat = (_concat, [(name_p, i) for i in range(npartitions)]) dsk[(name, 0)] = (head, concat, n) else: dsk = {(name, 0): (head, (self._name, 0), n)} graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) result = new_dd_object( graph, name, self._meta, [self.divisions[0], self.divisions[npartitions]] ) if compute: result = result.compute() return result def tail(self, n=5, compute=True): """Last n rows of the dataset Caveat, the only checks the last n rows of the last partition. """ name = "tail-%d-%s" % (n, self._name) dsk = {(name, 0): (M.tail, (self._name, self.npartitions - 1), n)} graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) result = new_dd_object(graph, name, self._meta, self.divisions[-2:]) if compute: result = result.compute() return result @property def loc(self): """Purely label-location based indexer for selection by label. >>> df.loc["b"] # doctest: +SKIP >>> df.loc["b":"d"] # doctest: +SKIP """ from dask.dataframe.indexing import _LocIndexer return _LocIndexer(self) def _partitions(self, index): if not isinstance(index, tuple): index = (index,) from dask.array.slicing import normalize_index index = normalize_index(index, (self.npartitions,)) index = tuple(slice(k, k + 1) if isinstance(k, Number) else k for k in index) name = "blocks-" + tokenize(self, index) new_keys = np.array(self.__dask_keys__(), dtype=object)[index].tolist() divisions = [self.divisions[i] for _, i in new_keys] + [ self.divisions[new_keys[-1][1] + 1] ] dsk = {(name, i): tuple(key) for i, key in enumerate(new_keys)} graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, self._meta, divisions) @property def partitions(self): """Slice dataframe by partitions This allows partitionwise slicing of a Dask Dataframe. You can perform normal Numpy-style slicing, but now rather than slice elements of the array you slice along partitions so, for example, ``df.partitions[:5]`` produces a new Dask Dataframe of the first five partitions. Valid indexers are integers, sequences of integers, slices, or boolean masks. Examples -------- >>> df.partitions[0] # doctest: +SKIP >>> df.partitions[:3] # doctest: +SKIP >>> df.partitions[::10] # doctest: +SKIP Returns ------- A Dask DataFrame """ return IndexCallable(self._partitions) # Note: iloc is implemented only on DataFrame def repartition( self, divisions=None, npartitions=None, partition_size=None, freq=None, force=False, ): """Repartition dataframe along new divisions Parameters ---------- divisions : list, optional The "dividing lines" used to split the dataframe into partitions. For ``divisions=[0, 10, 50, 100]``, there would be three output partitions, where the new index contained [0, 10), [10, 50), and [50, 100), respectively. See https://docs.dask.org/en/latest/dataframe-design.html#partitions. Only used if npartitions and partition_size isn't specified. For convenience if given an integer this will defer to npartitions and if given a string it will defer to partition_size (see below) npartitions : int, optional Approximate number of partitions of output. Only used if partition_size isn't specified. The number of partitions used may be slightly lower than npartitions depending on data distribution, but will never be higher. partition_size: int or string, optional Max number of bytes of memory for each partition. Use numbers or strings like 5MB. If specified npartitions and divisions will be ignored. Note that the size reflects the number of bytes used as computed by ``pandas.DataFrame.memory_usage``, which will not necessarily match the size when storing to disk. .. warning:: This keyword argument triggers computation to determine the memory size of each partition, which may be expensive. freq : str, pd.Timedelta A period on which to partition timeseries data like ``'7D'`` or ``'12h'`` or ``pd.Timedelta(hours=12)``. Assumes a datetime index. force : bool, default False Allows the expansion of the existing divisions. If False then the new divisions' lower and upper bounds must be the same as the old divisions'. Notes ----- Exactly one of `divisions`, `npartitions`, `partition_size`, or `freq` should be specified. A ``ValueError`` will be raised when that is not the case. Also note that ``len(divisions)`` is equal to ``npartitions + 1``. This is because ``divisions`` represents the upper and lower bounds of each partition. The first item is the lower bound of the first partition, the second item is the lower bound of the second partition and the upper bound of the first partition, and so on. The second-to-last item is the lower bound of the last partition, and the last (extra) item is the upper bound of the last partition. Examples -------- >>> df = df.repartition(npartitions=10) # doctest: +SKIP >>> df = df.repartition(divisions=[0, 5, 10, 20]) # doctest: +SKIP >>> df = df.repartition(freq='7d') # doctest: +SKIP See Also -------- DataFrame.memory_usage_per_partition pandas.DataFrame.memory_usage """ if isinstance(divisions, int): warnings.warn( "divisions is an integer and will be inferred as npartitions instead. " "This automatic inference is deprecated and will change in the future. " f"Please set npartitions={divisions} instead.", FutureWarning, stacklevel=2, ) npartitions = divisions divisions = None if isinstance(divisions, str): warnings.warn( "divisions is a string and will be inferred as partition_size instead. " "This automatic inference is deprecated and will change in the future. " f"Please set partition_size={divisions} instead.", FutureWarning, stacklevel=2, ) partition_size = divisions divisions = None if ( sum( [ partition_size is not None, divisions is not None, npartitions is not None, freq is not None, ] ) != 1 ): raise ValueError( "Please provide exactly one of ``npartitions=``, ``freq=``, " "``divisions=``, ``partition_size=`` keyword arguments" ) if partition_size is not None: return repartition_size(self, partition_size) elif npartitions is not None: return repartition_npartitions(self, npartitions) elif divisions is not None: return repartition(self, divisions, force=force) elif freq is not None: return repartition_freq(self, freq=freq) @_deprecated_kwarg("shuffle", "shuffle_method") def shuffle( self, on, npartitions=None, max_branch=None, shuffle_method=None, ignore_index=False, compute=None, ): """Rearrange DataFrame into new partitions Uses hashing of `on` to map rows to output partitions. After this operation, rows with the same value of `on` will be in the same partition. Parameters ---------- on : str, list of str, or Series, Index, or DataFrame Column(s) or index to be used to map rows to output partitions npartitions : int, optional Number of partitions of output. Partition count will not be changed by default. max_branch: int, optional The maximum number of splits per input partition. Used within the staged shuffling algorithm. shuffle_method: {'disk', 'tasks', 'p2p'}, optional Either ``'disk'`` for single-node operation or ``'tasks'`` and ``'p2p'`` for distributed operation. Will be inferred by your current scheduler. ignore_index: bool, default False Ignore index during shuffle. If ``True``, performance may improve, but index values will not be preserved. compute: bool Whether or not to trigger an immediate computation. Defaults to False. Notes ----- This does not preserve a meaningful index/partitioning scheme. This is not deterministic if done in parallel. Examples -------- >>> df = df.shuffle(df.columns[0]) # doctest: +SKIP """ from dask.dataframe.shuffle import shuffle as dd_shuffle return dd_shuffle( self, on, npartitions=npartitions, max_branch=max_branch, shuffle_method=shuffle_method, ignore_index=ignore_index, compute=compute, ) def _limit_fillna(self, method=None, *, limit=None, skip_check=None, meta=None): if limit is None: name = "fillna-chunk-" + tokenize(self, method) dsk = { (name, i): ( methods.fillna_check, (self._name, i), method, i != skip_check, ) for i in range(self.npartitions) } graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, meta, self.divisions) else: return self @_deprecated_kwarg("method", None, comment="Use ffill or bfill instead.") @derived_from(pd.DataFrame) def fillna(self, value=None, method=None, limit=None, axis=None): if method is None and limit is not None: raise NotImplementedError("fillna with set limit and method=None") axis = self._validate_axis(axis) test_value = ( value._meta_nonempty if isinstance(value, (_Frame, Scalar)) else value ) # let it raise a FutureWarning if `method` is not None meta = self._meta_nonempty.fillna( value=test_value, method=method, limit=limit, axis=axis ) if method is None: # Control whether or not dask's partition alignment happens. # We don't want for a pandas Series. # We do want it for a dask Series if is_series_like(value) and not is_dask_collection(value): args = () kwargs = {"value": value} else: args = (value,) kwargs = {} return self.map_partitions( M.fillna, *args, limit=limit, axis=axis, meta=meta, enforce_metadata=False, **kwargs, ) elif method in ("pad", "ffill"): return self.ffill(limit=limit, axis=axis) else: return self.bfill(limit=limit, axis=axis) @derived_from(pd.DataFrame) def ffill(self, axis=None, limit=None): axis = self._validate_axis(axis) meta = self._meta_nonempty.ffill(limit=limit, axis=axis) if axis == 1: return self.map_partitions( M.ffill, limit=limit, axis=axis, meta=meta, enforce_metadata=False ) before, after = 1 if limit is None else limit, 0 parts = self._limit_fillna("ffill", limit=limit, skip_check=0, meta=meta) return parts.map_overlap(M.ffill, before, after, limit=limit, meta=meta) @derived_from(pd.DataFrame) def bfill(self, axis=None, limit=None): axis = self._validate_axis(axis) meta = self._meta_nonempty.bfill(limit=limit, axis=axis) if axis == 1: return self.map_partitions( M.bfill, limit=limit, axis=axis, meta=meta, enforce_metadata=False ) before, after = 0, 1 if limit is None else limit parts = self._limit_fillna( "bfill", limit=limit, skip_check=self.npartitions - 1, meta=meta ) return parts.map_overlap(M.bfill, before, after, limit=limit, meta=meta) def sample(self, n=None, frac=None, replace=False, random_state=None): """Random sample of items Parameters ---------- n : int, optional Number of items to return is not supported by dask. Use frac instead. frac : float, optional Approximate fraction of items to return. This sampling fraction is applied to all partitions equally. Note that this is an **approximate fraction**. You should not expect exactly ``len(df) * frac`` items to be returned, as the exact number of elements selected will depend on how your data is partitioned (but should be pretty close in practice). replace : boolean, optional Sample with or without replacement. Default = False. random_state : int or ``np.random.RandomState`` If an int, we create a new RandomState with this as the seed; Otherwise we draw from the passed RandomState. See Also -------- DataFrame.random_split pandas.DataFrame.sample """ if n is not None: msg = ( "sample does not support the number of sampled items " "parameter, 'n'. Please use the 'frac' parameter instead." ) if isinstance(n, Number) and 0 <= n <= 1: warnings.warn(msg) frac = n else: raise ValueError(msg) if frac is None: raise ValueError("frac must not be None") if random_state is None: random_state = np.random.RandomState() name = "sample-" + tokenize(self, frac, replace, random_state) state_data = random_state_data(self.npartitions, random_state) dsk = { (name, i): (methods.sample, (self._name, i), state, frac, replace) for i, state in enumerate(state_data) } graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, self._meta, self.divisions) @derived_from(pd.DataFrame) def replace(self, to_replace=None, value=None, regex=False): # In PANDAS_GE_140 pandas starts using no_default instead of None value_kwarg = {"value": value} if value is not None else {} return self.map_partitions( M.replace, to_replace=to_replace, **value_kwarg, regex=regex, enforce_metadata=False, ) def to_dask_array(self, lengths=None, meta=None): """Convert a dask DataFrame to a dask array. Parameters ---------- lengths : bool or Sequence of ints, optional How to determine the chunks sizes for the output array. By default, the output array will have unknown chunk lengths along the first axis, which can cause some later operations to fail. * True : immediately compute the length of each partition * Sequence : a sequence of integers to use for the chunk sizes on the first axis. These values are *not* validated for correctness, beyond ensuring that the number of items matches the number of partitions. meta : object, optional An optional `meta` parameter can be passed for dask to override the default metadata on the underlying dask array. Returns ------- """ if lengths is True: lengths = tuple(self.map_partitions(len, enforce_metadata=False).compute()) arr = self.values chunks = self._validate_chunks(arr, lengths) arr._chunks = chunks if meta is not None: arr._meta = meta return arr def to_hdf(self, path_or_buf, key, mode="a", append=False, **kwargs): """See dd.to_hdf docstring for more information""" from dask.dataframe.io import to_hdf return to_hdf(self, path_or_buf, key, mode, append, **kwargs) def to_csv(self, filename, **kwargs): """See dd.to_csv docstring for more information""" from dask.dataframe.io import to_csv return to_csv(self, filename, **kwargs) def to_sql( self, name: str, uri: str, schema=None, if_exists: str = "fail", index: bool = True, index_label=None, chunksize=None, dtype=None, method=None, compute=True, parallel=False, engine_kwargs=None, ): """See dd.to_sql docstring for more information""" from dask.dataframe.io import to_sql return to_sql( self, name=name, uri=uri, schema=schema, if_exists=if_exists, index=index, index_label=index_label, chunksize=chunksize, dtype=dtype, method=method, compute=compute, parallel=parallel, engine_kwargs=engine_kwargs, ) def to_json(self, filename, *args, **kwargs): """See dd.to_json docstring for more information""" from dask.dataframe.io import to_json return to_json(self, filename, *args, **kwargs) def to_delayed(self, optimize_graph=True): """Convert into a list of ``dask.delayed`` objects, one per partition. Parameters ---------- optimize_graph : bool, optional If True [default], the graph is optimized before converting into ``dask.delayed`` objects. Examples -------- >>> partitions = df.to_delayed() # doctest: +SKIP See Also -------- dask.dataframe.from_delayed """ keys = self.__dask_keys__() graph = self.__dask_graph__() layer = self.__dask_layers__()[0] if optimize_graph: graph = self.__dask_optimize__(graph, self.__dask_keys__()) layer = "delayed-" + self._name graph = HighLevelGraph.from_collections(layer, graph, dependencies=()) return [Delayed(k, graph, layer=layer) for k in keys] def to_backend(self, backend: str | None = None, **kwargs): """Move to a new DataFrame backend Parameters ---------- backend : str, Optional The name of the new backend to move to. The default is the current "dataframe.backend" configuration. Returns ------- DataFrame, Series or Index """ from dask.dataframe.io import to_backend return to_backend(self, backend=backend, **kwargs) @classmethod def _get_unary_operator(cls, op): return lambda self: elemwise(op, self) @classmethod def _get_binary_operator(cls, op, inv=False): if inv: return lambda self, other: elemwise(op, other, self) else: return lambda self, other: elemwise(op, self, other) @_deprecated_kwarg("axis", None) def rolling( self, window, min_periods=None, center=False, win_type=None, axis=no_default ): """Provides rolling transformations. Parameters ---------- window : int, str, offset Size of the moving window. This is the number of observations used for calculating the statistic. When not using a ``DatetimeIndex``, the window size must not be so large as to span more than one adjacent partition. If using an offset or offset alias like '5D', the data must have a ``DatetimeIndex`` .. versionchanged:: 0.15.0 Now accepts offsets and string offset aliases 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, str, None, default 0 This parameter is deprecated with ``pandas>=2.1``. Returns ------- a Rolling object on which to call a method to compute a statistic """ from dask.dataframe.rolling import Rolling if isinstance(window, Integral): if window < 0: raise ValueError("window must be >= 0") 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 Rolling( self, window=window, min_periods=min_periods, center=center, win_type=win_type, axis=axis, ) @derived_from(pd.DataFrame) def diff(self, periods=1, axis=0): """ .. note:: Pandas currently uses an ``object``-dtype column to represent boolean data with missing values. This can cause issues for boolean-specific operations, like ``|``. To enable boolean- specific operations, at the cost of metadata that doesn't match pandas, use ``.astype(bool)`` after the ``shift``. """ axis = self._validate_axis(axis) if not isinstance(periods, Integral): raise TypeError("periods must be an integer") if axis == 1: return self.map_partitions( M.diff, token="diff", periods=periods, axis=1, enforce_metadata=False ) before, after = (periods, 0) if periods > 0 else (0, -periods) return self.map_overlap(M.diff, before, after, token="diff", periods=periods) @derived_from(pd.DataFrame) def shift(self, periods=1, freq=None, axis=0): axis = self._validate_axis(axis) if not isinstance(periods, Integral): raise TypeError("periods must be an integer") if axis == 1: return self.map_partitions( M.shift, token="shift", periods=periods, freq=freq, axis=1, enforce_metadata=False, ) if freq is None: before, after = (periods, 0) if periods > 0 else (0, -periods) return self.map_overlap( M.shift, before, after, token="shift", periods=periods ) # Let pandas error on invalid arguments meta = self._meta_nonempty.shift(periods, freq=freq) out = self.map_partitions( M.shift, token="shift", periods=periods, freq=freq, meta=meta, enforce_metadata=False, transform_divisions=False, ) return maybe_shift_divisions(out, periods, freq=freq) def _reduction_agg( self, name, axis=None, skipna=True, split_every=False, out=None, numeric_only=None, none_is_zero=True, ): axis = self._validate_axis(axis, none_is_zero=none_is_zero) if has_keyword(getattr(self._meta_nonempty, name), "numeric_only"): numeric_only_kwargs = {"numeric_only": numeric_only} else: numeric_only_kwargs = {} with check_numeric_only_deprecation(name, True): meta = getattr(self._meta_nonempty, name)( axis=axis, skipna=skipna, **numeric_only_kwargs ) token = self._token_prefix + name if axis == 1: result = self.map_partitions( _getattr_numeric_only, meta=meta, token=token, skipna=skipna, axis=axis, _dask_method_name=name, **numeric_only_kwargs, ) return handle_out(out, result) else: result = self.reduction( _getattr_numeric_only, meta=meta, token=token, skipna=skipna, axis=axis, split_every=split_every, _dask_method_name=name, **numeric_only_kwargs, ) if isinstance(self, DataFrame) and isinstance(result, Series): result.divisions = (self.columns.min(), self.columns.max()) return handle_out(out, result) @derived_from(pd.DataFrame) def add_prefix(self, prefix): res = self.map_partitions(M.add_prefix, prefix) if self.known_divisions and is_series_like(self): res.divisions = tuple(prefix + str(division) for division in self.divisions) return res @derived_from(pd.DataFrame) def add_suffix(self, suffix): res = self.map_partitions(M.add_suffix, suffix) if self.known_divisions and is_series_like(self): res.divisions = tuple(str(division) + suffix for division in self.divisions) return res @derived_from(pd.DataFrame) def abs(self): _raise_if_object_series(self, "abs") meta = self._meta_nonempty.abs() return self.map_partitions(M.abs, meta=meta, enforce_metadata=False) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def all(self, axis=None, skipna=True, split_every=False, out=None): return self._reduction_agg( "all", axis=axis, skipna=skipna, split_every=split_every, out=out ) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def any(self, axis=None, skipna=True, split_every=False, out=None): return self._reduction_agg( "any", axis=axis, skipna=skipna, split_every=split_every, out=out ) @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @derived_from(pd.DataFrame) def sum( self, axis=None, skipna=True, split_every=False, dtype=None, out=None, min_count=None, numeric_only=None, ): result = self._reduction_agg( "sum", axis=axis, skipna=skipna, split_every=split_every, out=out, numeric_only=numeric_only, ) if min_count: cond = self.notnull().sum(axis=axis) >= min_count if is_series_like(cond): return result.where(cond, other=np.nan) else: return _scalar_binary( lambda x, y: result if x is y else np.nan, cond, True ) else: return result @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @derived_from(pd.DataFrame) def prod( self, axis=None, skipna=True, split_every=False, dtype=None, out=None, min_count=None, numeric_only=None, ): result = self._reduction_agg( "prod", axis=axis, skipna=skipna, split_every=split_every, out=out, numeric_only=numeric_only, ) if min_count: cond = self.notnull().sum(axis=axis) >= min_count if is_series_like(cond): return result.where(cond, other=np.nan) else: return _scalar_binary( lambda x, y: result if x is y else np.nan, cond, True ) else: return result product = prod # aliased dd.product @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def max(self, axis=0, skipna=True, split_every=False, out=None, numeric_only=None): if ( PANDAS_GE_140 and not PANDAS_GE_200 and axis is None and isinstance(self, DataFrame) ): warnings.warn( "In a future version, DataFrame.max(axis=None) will return a scalar max over the entire DataFrame. " "To retain the old behavior, use 'frame.max(axis=0)' or just 'frame.max()'", FutureWarning, ) axis = 0 return self._reduction_agg( "max", axis=axis, skipna=skipna, split_every=split_every, out=out, # Starting in pandas 2.0, `axis=None` does a full aggregation across both axes none_is_zero=not PANDAS_GE_200, numeric_only=numeric_only, ) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def min(self, axis=0, skipna=True, split_every=False, out=None, numeric_only=None): if ( PANDAS_GE_140 and not PANDAS_GE_200 and axis is None and isinstance(self, DataFrame) ): warnings.warn( "In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. " "To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()'", FutureWarning, ) axis = 0 return self._reduction_agg( "min", axis=axis, skipna=skipna, split_every=split_every, out=out, # Starting in pandas 2.0, `axis=None` does a full aggregation across both axes none_is_zero=not PANDAS_GE_200, numeric_only=numeric_only, ) @derived_from(pd.DataFrame) def idxmax( self, axis=None, skipna=True, split_every=False, numeric_only=no_default ): fn = "idxmax" axis = self._validate_axis(axis) numeric_only_kwargs = check_numeric_only_valid(numeric_only, "idxmax") meta = self._meta_nonempty.idxmax( axis=axis, skipna=skipna, **numeric_only_kwargs ) if axis == 1: return map_partitions( M.idxmax, self, meta=meta, token=self._token_prefix + fn, skipna=skipna, axis=axis, enforce_metadata=False, **numeric_only_kwargs, ) else: scalar = not is_series_like(meta) result = aca( [self], chunk=idxmaxmin_chunk, aggregate=idxmaxmin_agg, combine=idxmaxmin_combine, meta=meta, aggregate_kwargs={"scalar": scalar}, token=self._token_prefix + fn, split_every=split_every, skipna=skipna, fn=fn, **numeric_only_kwargs, ) if isinstance(self, DataFrame): result.divisions = (min(self.columns), max(self.columns)) return result @derived_from(pd.DataFrame) def idxmin( self, axis=None, skipna=True, split_every=False, numeric_only=no_default ): fn = "idxmin" axis = self._validate_axis(axis) numeric_only_kwargs = check_numeric_only_valid(numeric_only, "idxmax") meta = self._meta_nonempty.idxmax( axis=axis, skipna=skipna, **numeric_only_kwargs ) if axis == 1: return map_partitions( M.idxmin, self, meta=meta, token=self._token_prefix + fn, skipna=skipna, axis=axis, enforce_metadata=False, **numeric_only_kwargs, ) else: scalar = not is_series_like(meta) result = aca( [self], chunk=idxmaxmin_chunk, aggregate=idxmaxmin_agg, combine=idxmaxmin_combine, meta=meta, aggregate_kwargs={"scalar": scalar}, token=self._token_prefix + fn, split_every=split_every, skipna=skipna, fn=fn, **numeric_only_kwargs, ) if isinstance(self, DataFrame): result.divisions = (min(self.columns), max(self.columns)) return result @derived_from(pd.DataFrame) def count(self, axis=None, split_every=False, numeric_only=False): # This method is shared by DataFrame / Series, but only DataFrame # supports `numeric_only=`. Handle accordingly here. numeric_only_kwargs = {} if is_dataframe_like(self): numeric_only_kwargs = get_numeric_only_kwargs(numeric_only) axis = self._validate_axis(axis) token = self._token_prefix + "count" if axis == 1: meta = self._meta_nonempty.count(axis=axis, **numeric_only_kwargs) return self.map_partitions( M.count, meta=meta, token=token, axis=axis, enforce_metadata=False, **numeric_only_kwargs, ) else: meta = self._meta_nonempty.count() # Need the astype(int) for empty dataframes, which sum to float dtype result = self.reduction( M.count, aggregate=_count_aggregate, meta=meta, token=token, split_every=split_every, chunk_kwargs=numeric_only_kwargs, ) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return result @derived_from(pd.DataFrame) def mode(self, dropna=True, split_every=False): mode_series = self.reduction( chunk=M.value_counts, combine=M.sum, aggregate=_mode_aggregate, split_every=split_every, chunk_kwargs={"dropna": dropna}, aggregate_kwargs={"dropna": dropna}, ) mode_series.name = self.name return mode_series @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @_numeric_only @derived_from(pd.DataFrame) def mean( self, axis=0, skipna=True, split_every=False, dtype=None, out=None, numeric_only=None, ): if ( PANDAS_GE_140 and not PANDAS_GE_200 and axis is None and isinstance(self, DataFrame) ): warnings.warn( "In a future version, DataFrame.mean(axis=None) will return a scalar mean over the entire DataFrame. " "To retain the old behavior, use 'frame.mean(axis=0)' or just 'frame.mean()'", FutureWarning, ) axis = self._validate_axis(axis, none_is_zero=not PANDAS_GE_200) _raise_if_object_series(self, "mean") # NOTE: Do we want to warn here? with check_numeric_only_deprecation(), check_nuisance_columns_warning(): meta = self._meta_nonempty.mean( axis=axis, skipna=skipna, numeric_only=numeric_only ) if axis == 1: result = map_partitions( M.mean, self, meta=meta, token=self._token_prefix + "mean", axis=axis, skipna=skipna, enforce_metadata=False, numeric_only=numeric_only, ) return handle_out(out, result) else: num = self._get_numeric_data() s = num.sum(skipna=skipna, split_every=split_every) n = num.count(split_every=split_every) # Starting in pandas 2.0, `axis=None` does a full aggregation across both axes if PANDAS_GE_200 and axis is None and isinstance(self, DataFrame): result = s.sum() / n.sum() else: name = self._token_prefix + "mean-%s" % tokenize(self, axis, skipna) result = map_partitions( methods.mean_aggregate, s, n, token=name, meta=meta, enforce_metadata=False, parent_meta=self._meta, ) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return handle_out(out, result) def median_approximate( self, axis=None, method="default", ): """Return the approximate median of the values over the requested axis. Parameters ---------- axis : {0, 1, "index", "columns"} (default 0) 0 or ``"index"`` for row-wise, 1 or ``"columns"`` for column-wise method : {'default', 'tdigest', 'dask'}, optional What method to use. By default will use Dask's internal custom algorithm (``"dask"``). If set to ``"tdigest"`` will use tdigest for floats and ints and fallback to the ``"dask"`` otherwise. """ return self.quantile(q=0.5, axis=axis, method=method).rename(None) @derived_from(pd.DataFrame) def median(self, axis=None, method="default"): if axis in (1, "columns") or self.npartitions == 1: # Can provide an exact median in these cases return self.median_approximate(axis=axis, method=method) raise NotImplementedError( "Dask doesn't implement an exact median in all cases as this is hard to do in parallel. " "See the `median_approximate` method instead, which uses an approximate algorithm." ) @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @derived_from(pd.DataFrame) def var( self, axis=None, skipna=True, ddof=1, split_every=False, dtype=None, out=None, numeric_only=no_default, ): axis = self._validate_axis(axis) _raise_if_object_series(self, "var") numeric_only_kwargs = get_numeric_only_kwargs(numeric_only) with check_numeric_only_deprecation( "var", True ), check_reductions_runtime_warning(): meta = self._meta_nonempty.var( axis=axis, skipna=skipna, **numeric_only_kwargs, ) if axis == 1: result = map_partitions( M.var, self, meta=meta, token=self._token_prefix + "var", axis=axis, skipna=skipna, ddof=ddof, enforce_metadata=False, **numeric_only_kwargs, ) return handle_out(out, result) else: if self.ndim == 1: result = self._var_1d(self, skipna, ddof, split_every) return handle_out(out, result) # pandas 1.0+ does not implement var on timedelta result = self._var_numeric(skipna, ddof, split_every) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return handle_out(out, result) def _var_numeric(self, skipna=True, ddof=1, split_every=False): num = self.select_dtypes(include=["number", "bool"], exclude=[np.timedelta64]) values_dtype = num.values.dtype array_values = num.values if not np.issubdtype(values_dtype, np.number): array_values = num.values.astype("f8") var = da.nanvar if skipna or skipna is None else da.var array_var = var(array_values, axis=0, ddof=ddof, split_every=split_every) name = self._token_prefix + "var-numeric" + tokenize(num, split_every) cols = num._meta.columns if is_dataframe_like(num) else None with check_reductions_runtime_warning(): meta_computation = num._meta_nonempty.var(axis=0) var_shape = meta_computation.shape array_var_name = (array_var._name,) + (0,) * len(var_shape) layer = {(name, 0): (methods.wrap_var_reduction, array_var_name, cols)} graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_var]) return new_dd_object(graph, name, meta_computation, divisions=[None, None]) def _var_1d(self, column, skipna=True, ddof=1, split_every=False): is_timedelta = is_timedelta64_dtype(column._meta) if is_timedelta: if not skipna: is_nan = column.isna() column = column.astype("i8") column = column.mask(is_nan) else: column = column.dropna().astype("i8") if pd.api.types.is_extension_array_dtype(column._meta_nonempty): # Don't have to worry about non-numeric, this raises earlier column = column.astype("f8") elif not np.issubdtype(column.dtype, np.number): column = column.astype("f8") name = self._token_prefix + "var-1d-" + tokenize(column, split_every) var = da.nanvar if skipna or skipna is None else da.var array_var = var(column.values, axis=0, ddof=ddof, split_every=split_every) layer = {(name, 0): (methods.wrap_var_reduction, (array_var._name,), None)} graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_var]) return new_dd_object( graph, name, column._meta_nonempty.var(), divisions=[None, None] ) @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @_numeric_data @derived_from(pd.DataFrame) def std( self, axis=None, skipna=True, ddof=1, split_every=False, dtype=None, out=None, numeric_only=no_default, ): axis = self._validate_axis(axis) _raise_if_object_series(self, "std") _raise_if_not_series_or_dataframe(self, "std") numeric_kwargs = get_numeric_only_kwargs(numeric_only) with check_numeric_only_deprecation( "std", True ), check_reductions_runtime_warning(): meta = self._meta_nonempty.std(axis=axis, skipna=skipna, **numeric_kwargs) is_df_like = is_dataframe_like(self._meta) needs_time_conversion = False numeric_dd = self if is_df_like: time_cols = self._meta.select_dtypes( include=["datetime", "timedelta"] ).columns if len(time_cols) > 0: ( numeric_dd, needs_time_conversion, ) = self._convert_time_cols_to_numeric(time_cols, axis, meta, skipna) else: needs_time_conversion = is_datetime64_any_dtype(self._meta) if needs_time_conversion: numeric_dd = _convert_to_numeric(self, skipna) if axis == 1: result = map_partitions( M.std if not needs_time_conversion else _sqrt_and_convert_to_timedelta, numeric_dd, meta=meta, token=self._token_prefix + "std", axis=axis, skipna=skipna, ddof=ddof, enforce_metadata=False, **numeric_kwargs, parent_meta=self._meta, ) return handle_out(out, result) # Case where axis=0 or axis=None v = numeric_dd.var(skipna=skipna, ddof=ddof, split_every=split_every) name = self._token_prefix + "std" if needs_time_conversion: sqrt_func_kwargs = { "is_df_like": is_df_like, "time_cols": time_cols if is_df_like else None, "axis": axis, "dtype": getattr(meta, "dtype", None), } sqrt_func = _sqrt_and_convert_to_timedelta else: sqrt_func_kwargs = {} sqrt_func = np.sqrt result = map_partitions( sqrt_func, v, meta=meta, token=name, enforce_metadata=False, parent_meta=self._meta, **sqrt_func_kwargs, ) # Try to match the Pandas result dtype if is_df_like and hasattr(meta, "dtype"): result = result.astype(meta.dtype) return handle_out(out, result) def _convert_time_cols_to_numeric(self, time_cols, axis, meta, skipna): from dask.dataframe.io import from_pandas needs_time_conversion = True # Ensure all columns are correct type. Need to shallow copy since cols will be modified if axis == 0: numeric_dd = self[meta.index].copy() else: numeric_dd = self.copy() # Mix of datetimes with other numeric types produces NaNs for each value in std() series if axis == 1 and len(time_cols) != len(self.columns): # This is faster than converting each column to numeric when it's not necessary # since each standard deviation will just be NaN needs_time_conversion = False numeric_dd = from_pandas( meta_frame_constructor(self)( {"_": meta_series_constructor(self)([np.nan])}, index=self.index, ), npartitions=self.npartitions, ) else: # Convert timedelta and datetime columns to integer types so we can use var for col in time_cols: numeric_dd[col] = _convert_to_numeric(numeric_dd[col], skipna) return numeric_dd, needs_time_conversion @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def skew( self, axis=0, bias=True, nan_policy="propagate", out=None, numeric_only=no_default, ): """ .. note:: This implementation follows the dask.array.stats implementation of skewness and calculates skewness without taking into account a bias term for finite sample size, which corresponds to the default settings of the scipy.stats skewness calculation. However, Pandas corrects for this, so the values differ by a factor of (n * (n - 1)) ** 0.5 / (n - 2), where n is the number of samples. Further, this method currently does not support filtering out NaN values, which is again a difference to Pandas. """ if PANDAS_GE_200 and axis is None: raise ValueError( "`axis=None` isn't currently supported for `skew` when using `pandas >=2` " f"(pandas={str(PANDAS_VERSION)} is installed)." ) axis = self._validate_axis(axis) _raise_if_object_series(self, "skew") numeric_only_kwargs = get_numeric_only_kwargs(numeric_only) if is_dataframe_like(self): # Let pandas raise errors if necessary meta = self._meta_nonempty.skew(axis=axis, **numeric_only_kwargs) else: meta = self._meta_nonempty.skew() if axis == 1: result = map_partitions( M.skew, self, meta=meta, token=self._token_prefix + "skew", axis=axis, enforce_metadata=False, ) return handle_out(out, result) else: if self.ndim == 1: result = self._skew_1d(self, bias=bias, nan_policy=nan_policy) return handle_out(out, result) else: result = self._skew_numeric(bias=bias, nan_policy=nan_policy) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return handle_out(out, result) def _skew_1d(self, column, bias=True, nan_policy="propagate"): """1D version of the skew calculation. Uses the array version from da.stats in case we are passing in a single series """ # import depends on scipy, not installed by default from dask.array import stats as da_stats if pd.api.types.is_extension_array_dtype(column._meta_nonempty): column = column.astype("f8") elif not np.issubdtype(column.dtype, np.number): column = column.astype("f8") name = self._token_prefix + "skew-1d-" + tokenize(column) array_skew = da_stats.skew( column.values, axis=0, bias=bias, nan_policy=nan_policy ) layer = {(name, 0): (methods.wrap_skew_reduction, (array_skew._name,), None)} graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_skew]) return new_dd_object( graph, name, column._meta_nonempty.skew(), divisions=[None, None] ) def _skew_numeric(self, bias=True, nan_policy="propagate"): """Method for dataframes with numeric columns. Maps the array version from da.stats onto the numeric array of columns. """ # import depends on scipy, not installed by default from dask.array import stats as da_stats num = self.select_dtypes(include=["number", "bool"], exclude=[np.timedelta64]) values_dtype = num.values.dtype array_values = num.values if not np.issubdtype(values_dtype, np.number): array_values = num.values.astype("f8") array_skew = da_stats.skew( array_values, axis=0, bias=bias, nan_policy=nan_policy ) name = self._token_prefix + "var-numeric" + tokenize(num) cols = num._meta.columns if is_dataframe_like(num) else None with check_reductions_runtime_warning(): skew_shape = num._meta_nonempty.var(axis=0).shape array_skew_name = (array_skew._name,) + (0,) * len(skew_shape) layer = {(name, 0): (methods.wrap_skew_reduction, array_skew_name, cols)} graph = HighLevelGraph.from_collections(name, layer, dependencies=[array_skew]) return new_dd_object( graph, name, num._meta_nonempty.skew(), divisions=[None, None] ) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def kurtosis( self, axis=0, fisher=True, bias=True, nan_policy="propagate", out=None, numeric_only=no_default, ): """ .. note:: This implementation follows the dask.array.stats implementation of kurtosis and calculates kurtosis without taking into account a bias term for finite sample size, which corresponds to the default settings of the scipy.stats kurtosis calculation. This differs from pandas. Further, this method currently does not support filtering out NaN values, which is again a difference to Pandas. """ if PANDAS_GE_200 and axis is None: raise ValueError( "`axis=None` isn't currently supported for `kurtosis` when using `pandas >=2` " f"(pandas={str(PANDAS_VERSION)} is installed)." ) axis = self._validate_axis(axis) _raise_if_object_series(self, "kurtosis") numeric_only_kwargs = get_numeric_only_kwargs(numeric_only) if is_dataframe_like(self): # Let pandas raise errors if necessary meta = self._meta_nonempty.kurtosis(axis=axis, **numeric_only_kwargs) else: meta = self._meta_nonempty.kurtosis() if axis == 1: result = map_partitions( M.kurtosis, self, meta=meta, token=self._token_prefix + "kurtosis", axis=axis, enforce_metadata=False, ) return handle_out(out, result) else: if self.ndim == 1: result = self._kurtosis_1d( self, fisher=fisher, bias=bias, nan_policy=nan_policy ) return handle_out(out, result) else: result = self._kurtosis_numeric( fisher=fisher, bias=bias, nan_policy=nan_policy ) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return handle_out(out, result) def _kurtosis_1d(self, column, fisher=True, bias=True, nan_policy="propagate"): """1D version of the kurtosis calculation. Uses the array version from da.stats in case we are passing in a single series """ # import depends on scipy, not installed by default from dask.array import stats as da_stats if pd.api.types.is_extension_array_dtype(column._meta_nonempty): column = column.astype("f8") elif not np.issubdtype(column.dtype, np.number): column = column.astype("f8") name = self._token_prefix + "kurtosis-1d-" + tokenize(column) array_kurtosis = da_stats.kurtosis( column.values, axis=0, fisher=fisher, bias=bias, nan_policy=nan_policy ) layer = { (name, 0): (methods.wrap_kurtosis_reduction, (array_kurtosis._name,), None) } graph = HighLevelGraph.from_collections( name, layer, dependencies=[array_kurtosis] ) return new_dd_object( graph, name, column._meta_nonempty.kurtosis(), divisions=[None, None] ) def _kurtosis_numeric(self, fisher=True, bias=True, nan_policy="propagate"): """Method for dataframes with numeric columns. Maps the array version from da.stats onto the numeric array of columns. """ # import depends on scipy, not installed by default from dask.array import stats as da_stats num = self.select_dtypes(include=["number", "bool"], exclude=[np.timedelta64]) values_dtype = num.values.dtype array_values = num.values if not np.issubdtype(values_dtype, np.number): array_values = num.values.astype("f8") array_kurtosis = da_stats.kurtosis( array_values, axis=0, fisher=fisher, bias=bias, nan_policy=nan_policy ) name = self._token_prefix + "kurtosis-numeric" + tokenize(num) cols = num._meta.columns if is_dataframe_like(num) else None with check_reductions_runtime_warning(): kurtosis_shape = num._meta_nonempty.var(axis=0).shape array_kurtosis_name = (array_kurtosis._name,) + (0,) * len(kurtosis_shape) layer = { (name, 0): (methods.wrap_kurtosis_reduction, array_kurtosis_name, cols) } graph = HighLevelGraph.from_collections( name, layer, dependencies=[array_kurtosis] ) return new_dd_object( graph, name, num._meta_nonempty.kurtosis(), divisions=[None, None] ) @_numeric_only @derived_from(pd.DataFrame) def sem(self, axis=None, skipna=True, ddof=1, split_every=False, numeric_only=None): axis = self._validate_axis(axis) _raise_if_object_series(self, "sem") with check_numeric_only_deprecation(): meta = self._meta_nonempty.sem( axis=axis, skipna=skipna, ddof=ddof, numeric_only=numeric_only ) if axis == 1: return map_partitions( M.sem, self, meta=meta, token=self._token_prefix + "sem", axis=axis, skipna=skipna, ddof=ddof, parent_meta=self._meta, numeric_only=numeric_only, ) else: num = self._get_numeric_data() v = num.var(skipna=skipna, ddof=ddof, split_every=split_every) n = num.count(split_every=split_every) name = self._token_prefix + "sem" result = map_partitions( np.sqrt, v / n, meta=meta, token=name, enforce_metadata=False, parent_meta=self._meta, ) if isinstance(self, DataFrame): result.divisions = (self.columns.min(), self.columns.max()) return result def quantile(self, q=0.5, axis=0, numeric_only=no_default, method="default"): """Approximate row-wise and precise column-wise quantiles of DataFrame Parameters ---------- q : list/array of floats, default 0.5 (50%) Iterable of numbers ranging from 0 to 1 for the desired quantiles axis : {0, 1, 'index', 'columns'} (default 0) 0 or 'index' for row-wise, 1 or 'columns' for column-wise method : {'default', 'tdigest', 'dask'}, optional What method to use. By default will use dask's internal custom algorithm (``'dask'``). If set to ``'tdigest'`` will use tdigest for floats and ints and fallback to the ``'dask'`` otherwise. """ axis = self._validate_axis(axis) keyname = "quantiles-concat--" + tokenize(self, q, axis) numeric_kwargs = get_numeric_only_kwargs(numeric_only) with check_numeric_only_deprecation("quantile", True): meta = self._meta.quantile(q, axis=axis, **numeric_kwargs) if axis == 1: if isinstance(q, list): # Not supported, the result will have current index as columns raise ValueError("'q' must be scalar when axis=1 is specified") return map_partitions( _getattr_numeric_only, self, q, axis, token=keyname, enforce_metadata=False, **numeric_kwargs, meta=(q, "f8"), parent_meta=self._meta, _dask_method_name="quantile", ) else: _raise_if_object_series(self, "quantile") num = ( self._get_numeric_data() if numeric_only is True or (not PANDAS_GE_200 and numeric_only is no_default) else self ) quantiles = tuple( quantile(num.iloc[:, i], q, method) for i in range(len(num.columns)) ) qnames = [(_q._name, 0) for _q in quantiles] if isinstance(quantiles[0], Scalar): layer = { (keyname, 0): (type(meta), qnames, num.columns, None, meta.name) } graph = HighLevelGraph.from_collections( keyname, layer, dependencies=quantiles ) divisions = (min(num.columns), max(num.columns)) return Series(graph, keyname, meta, divisions) else: layer = {(keyname, 0): (methods.concat, qnames, 1)} graph = HighLevelGraph.from_collections( keyname, layer, dependencies=quantiles ) return new_dd_object(graph, keyname, meta, quantiles[0].divisions) @derived_from(pd.DataFrame) def describe( self, split_every=False, percentiles=None, percentiles_method="default", include=None, exclude=None, datetime_is_numeric=no_default, ): if PANDAS_GE_200: if datetime_is_numeric is no_default: datetime_is_numeric = True datetime_is_numeric_kwarg = {} else: raise TypeError( "datetime_is_numeric is removed in pandas>=2.0.0, datetime data will always be " "summarized as numeric" ) else: datetime_is_numeric = ( False if datetime_is_numeric is no_default else datetime_is_numeric ) datetime_is_numeric_kwarg = {"datetime_is_numeric": datetime_is_numeric} if self._meta.ndim == 1: meta = self._meta_nonempty.describe( percentiles=percentiles, include=include, exclude=exclude, **datetime_is_numeric_kwarg, ) output = self._describe_1d( self, split_every, percentiles, percentiles_method, datetime_is_numeric ) output._meta = meta return output elif (include is None) and (exclude is None): _include = [np.number, np.timedelta64] if datetime_is_numeric: _include.append(np.datetime64) data = self._meta.select_dtypes(include=_include) # when some numerics/timedeltas are found, by default keep them if len(data.columns) == 0: chosen_columns_indexes = list(range(len(self._meta.columns))) else: # check if there are timedelta, boolean, or datetime columns _include = [np.timedelta64, bool] if datetime_is_numeric: _include.append(np.datetime64) bools_and_times = self._meta.select_dtypes(include=_include) if len(bools_and_times.columns) == 0: return self._describe_numeric( self, split_every, percentiles, percentiles_method, ) else: chosen_columns_indexes = self._get_columns_indexes_based_on_dtypes( data ) elif include == "all": if exclude is not None: msg = "exclude must be None when include is 'all'" raise ValueError(msg) chosen_columns_indexes = list(range(len(self._meta.columns))) else: data = self._meta.select_dtypes(include=include, exclude=exclude) chosen_columns_indexes = self._get_columns_indexes_based_on_dtypes(data) stats = [ self._describe_1d( self.iloc[:, col_idx], split_every, percentiles, percentiles_method, datetime_is_numeric, ) for col_idx in chosen_columns_indexes ] stats_names = [(s._name, 0) for s in stats] name = "describe--" + tokenize(self, split_every) layer = {(name, 0): (methods.describe_aggregate, stats_names)} graph = HighLevelGraph.from_collections(name, layer, dependencies=stats) meta = self._meta_nonempty.describe( include=include, exclude=exclude, **datetime_is_numeric_kwarg ) return new_dd_object(graph, name, meta, divisions=[None, None]) def _get_columns_indexes_based_on_dtypes(self, subset): meta = self._meta.dtypes.reset_index() meta.index.name = "indexer" return ( meta.reset_index() .merge(subset.dtypes.reset_index(), how="inner")["indexer"] .values ) def _describe_1d( self, data, split_every=False, percentiles=None, percentiles_method="default", datetime_is_numeric=False, ): if is_bool_dtype(data._meta): return self._describe_nonnumeric_1d( data, split_every=split_every, datetime_is_numeric=datetime_is_numeric ) elif is_numeric_dtype(data._meta): return self._describe_numeric( data, split_every=split_every, percentiles=percentiles, percentiles_method=percentiles_method, ) elif is_timedelta64_dtype(data._meta): return self._describe_numeric( data.dropna(), split_every=split_every, percentiles=percentiles, percentiles_method=percentiles_method, is_timedelta_column=True, ) elif is_datetime64_any_dtype(data._meta) and datetime_is_numeric: return self._describe_numeric( data.dropna(), split_every=split_every, percentiles=percentiles, percentiles_method=percentiles_method, is_datetime_column=True, ) else: return self._describe_nonnumeric_1d( data, split_every=split_every, datetime_is_numeric=datetime_is_numeric ) def _describe_numeric( self, data, split_every=False, percentiles=None, percentiles_method="default", is_timedelta_column=False, is_datetime_column=False, ): from dask.dataframe.numeric import to_numeric if is_timedelta_column or is_datetime_column: num = to_numeric(data) else: num = data._get_numeric_data() if data.ndim == 2 and len(num.columns) == 0: raise ValueError("DataFrame contains only non-numeric data.") elif data.ndim == 1 and data.dtype == "object": raise ValueError("Cannot compute ``describe`` on object dtype.") if percentiles is None: percentiles = [0.25, 0.5, 0.75] else: # always include the the 50%tle to calculate the median # unique removes duplicates and sorts quantiles percentiles = np.array(percentiles) percentiles = np.append(percentiles, 0.5) percentiles = np.unique(percentiles) percentiles = list(percentiles) stats = [ num.count(split_every=split_every), num.mean(split_every=split_every), num.std(split_every=split_every), num.min(split_every=split_every), num.quantile(percentiles, method=percentiles_method), num.max(split_every=split_every), ] stats_names = [(s._name, 0) for s in stats] colname = data._meta.name if is_series_like(data._meta) else None name = "describe-numeric--" + tokenize(num, split_every) layer = { (name, 0): ( methods.describe_numeric_aggregate, stats_names, colname, is_timedelta_column, is_datetime_column, ) } graph = HighLevelGraph.from_collections(name, layer, dependencies=stats) meta = num._meta_nonempty.describe() return new_dd_object(graph, name, meta, divisions=[None, None]) def _describe_nonnumeric_1d( self, data, split_every=False, datetime_is_numeric=False ): from dask.dataframe.numeric import to_numeric vcounts = data.value_counts(split_every=split_every) count_nonzero = vcounts[vcounts != 0] count_unique = count_nonzero.size stats = [ # nunique count_unique, # count data.count(split_every=split_every), # most common value vcounts._head(1, npartitions=1, compute=False, safe=False), ] if is_datetime64_any_dtype(data._meta) and not datetime_is_numeric: min_ts = to_numeric(data.dropna()).min(split_every=split_every) max_ts = to_numeric(data.dropna()).max(split_every=split_every) stats.extend([min_ts, max_ts]) stats_names = [(s._name, 0) for s in stats] colname = data._meta.name name = "describe-nonnumeric-1d--" + tokenize(data, split_every) layer = { (name, 0): (methods.describe_nonnumeric_aggregate, stats_names, colname) } graph = HighLevelGraph.from_collections(name, layer, dependencies=stats) if not PANDAS_GE_200: datetime_is_numeric_kwarg = {"datetime_is_numeric": datetime_is_numeric} else: datetime_is_numeric_kwarg = {} meta = data._meta_nonempty.describe(**datetime_is_numeric_kwarg) return new_dd_object(graph, name, meta, divisions=[None, None]) def _cum_agg( self, op_name, chunk, aggregate, axis, skipna=True, chunk_kwargs=None, out=None, # Deprecated ): """Wrapper for cumulative operation""" axis = self._validate_axis(axis) if axis == 1: name = f"{self._token_prefix}{op_name}(axis=1)" result = self.map_partitions(chunk, token=name, **chunk_kwargs) return handle_out(out, result) else: # cumulate each partitions name1 = f"{self._token_prefix}{op_name}-map" cumpart = map_partitions( chunk, self, token=name1, meta=self, **chunk_kwargs ) name2 = f"{self._token_prefix}{op_name}-take-last" cumlast = map_partitions( _take_last, cumpart, skipna, meta=meta_series_constructor(self)([], dtype="float"), token=name2, ) suffix = tokenize(self) name = f"{self._token_prefix}{op_name}-{suffix}" cname = f"{self._token_prefix}{op_name}-cum-last-{suffix}" # aggregate cumulated partitions and its previous last element layer = {} layer[(name, 0)] = (cumpart._name, 0) for i in range(1, self.npartitions): # store each cumulative step to graph to reduce computation if i == 1: layer[(cname, i)] = (cumlast._name, i - 1) else: # aggregate with previous cumulation results layer[(cname, i)] = ( methods._cum_aggregate_apply, aggregate, (cname, i - 1), (cumlast._name, i - 1), ) layer[(name, i)] = (aggregate, (cumpart._name, i), (cname, i)) graph = HighLevelGraph.from_collections( name, layer, dependencies=[cumpart, cumlast] ) result = new_dd_object(graph, name, chunk(self._meta), self.divisions) return handle_out(out, result) @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @derived_from(pd.DataFrame) def cumsum(self, axis=None, skipna=True, dtype=None, out=None): return self._cum_agg( "cumsum", chunk=M.cumsum, aggregate=methods.cumsum_aggregate, axis=axis, skipna=skipna, chunk_kwargs=dict(axis=axis, skipna=skipna), out=out, ) @_dummy_numpy_dispatcher("dtype", "out", deprecated=True) @derived_from(pd.DataFrame) def cumprod(self, axis=None, skipna=True, dtype=None, out=None): return self._cum_agg( "cumprod", chunk=M.cumprod, aggregate=methods.cumprod_aggregate, axis=axis, skipna=skipna, chunk_kwargs=dict(axis=axis, skipna=skipna), out=out, ) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def cummax(self, axis=None, skipna=True, out=None): return self._cum_agg( "cummax", chunk=M.cummax, aggregate=methods.cummax_aggregate, axis=axis, skipna=skipna, chunk_kwargs=dict(axis=axis, skipna=skipna), out=out, ) @_dummy_numpy_dispatcher("out", deprecated=True) @derived_from(pd.DataFrame) def cummin(self, axis=None, skipna=True, out=None): return self._cum_agg( "cummin", chunk=M.cummin, aggregate=methods.cummin_aggregate, axis=axis, skipna=skipna, chunk_kwargs=dict(axis=axis, skipna=skipna), out=out, ) def _validate_condition(self, cond): cond_res = cond(self._meta) if callable(cond) else cond if not ( is_dask_collection(cond_res) or is_dataframe_like(cond_res) or is_series_like(cond_res) or is_index_like(cond_res) ): raise ValueError( f"Condition should be an object that can be aligned with {self.__class__}, " f" which includes Dask or pandas collections, DataFrames or Series, or a Callable." ) @derived_from(pd.DataFrame) def where(self, cond, other=np.nan): # cond and other may be dask instance, # passing map_partitions via keyword will not be aligned self._validate_condition(cond) return map_partitions(M.where, self, cond, other, enforce_metadata=False) @derived_from(pd.DataFrame) def mask(self, cond, other=np.nan): self._validate_condition(cond) return map_partitions(M.mask, self, cond, other, enforce_metadata=False) @derived_from(pd.DataFrame) def notnull(self): return self.map_partitions(M.notnull, enforce_metadata=False) @derived_from(pd.DataFrame) def isnull(self): return self.map_partitions(M.isnull, enforce_metadata=False) @derived_from(pd.DataFrame) def isna(self): return self.map_partitions(M.isna, enforce_metadata=False) @derived_from(pd.DataFrame) def isin(self, values): if is_dataframe_like(self._meta): # DataFrame.isin does weird alignment stuff bad_types = (_Frame, pd.Series, pd.DataFrame) else: bad_types = (_Frame,) if isinstance(values, bad_types): raise NotImplementedError("Passing a %r to `isin`" % typename(type(values))) meta = self._meta_nonempty.isin(values) # We wrap values in a delayed for two reasons: # - avoid serializing data in every task # - avoid cost of traversal of large list in optimizations if isinstance(values, list): # Motivated by https://github.com/dask/dask/issues/9411. This appears to be # caused by https://github.com/dask/distributed/issues/6368, and further # exacerbated by the fact that the list contains duplicates. This is a patch until # we can create a better fix for Serialization. try: values = list(set(values)) except TypeError: pass if not any(is_dask_collection(v) for v in values): try: values = np.fromiter(values, dtype=object) except ValueError: # Numpy 1.23 supports creating arrays of iterables, while lower # version 1.21.x and 1.22.x do not pass return self.map_partitions( M.isin, delayed(values), meta=meta, enforce_metadata=False ) @derived_from(pd.DataFrame) def astype(self, dtype): meta = self._meta.astype(dtype) if hasattr(dtype, "items"): set_unknown = [ k for k, v in dtype.items() if (isinstance(pd.api.types.pandas_dtype(v), pd.CategoricalDtype)) and getattr(v, "categories", None) is None ] meta = clear_known_categories(meta, cols=set_unknown) elif ( isinstance(pd.api.types.pandas_dtype(dtype), pd.CategoricalDtype) ) and getattr(dtype, "categories", None) is None: meta = clear_known_categories(meta) return self.map_partitions( M.astype, dtype=dtype, meta=meta, enforce_metadata=False ) if not PANDAS_GE_200: @derived_from(pd.Series) def append(self, other, interleave_partitions=False): if PANDAS_GE_140: warnings.warn( "The frame.append method is deprecated and will be removed from" "dask in a future version. Use dask.dataframe.concat instead.", FutureWarning, ) # because DataFrame.append will override the method, # wrap by pd.Series.append docstring from dask.dataframe.multi import concat if isinstance(other, (list, dict)): msg = "append doesn't support list or dict input" raise NotImplementedError(msg) return concat( [self, other], join="outer", interleave_partitions=interleave_partitions ) @derived_from(pd.Series) def dot(self, other, meta=no_default): if not isinstance(other, _Frame): raise TypeError("The second operand must be a dask array or dask dataframe") if isinstance(other, DataFrame): s = self.map_partitions(M.dot, other, token="dot", meta=meta) return s.groupby(by=s.index).apply( lambda x: x.sum(skipna=False), meta=s._meta_nonempty ) def _dot_series(*args, **kwargs): # .sum() is invoked on each partition before being applied to all # partitions. The return type is expected to be a series, not a numpy object return meta_series_constructor(self)(M.dot(*args, **kwargs)) return self.map_partitions(_dot_series, other, token="dot", meta=meta).sum( skipna=False ) @derived_from(pd.DataFrame) def align(self, other, join="outer", axis=None, fill_value=None): meta1, meta2 = _emulate( M.align, self, other, join, axis=axis, fill_value=fill_value ) aligned = self.map_partitions( M.align, other, join=join, axis=axis, fill_value=fill_value, enforce_metadata=False, ) token = tokenize(self, other, join, axis, fill_value) name1 = "align1-" + token dsk1 = { (name1, i): (getitem, key, 0) for i, key in enumerate(aligned.__dask_keys__()) } dsk1.update(aligned.dask) result1 = new_dd_object(dsk1, name1, meta1, aligned.divisions) name2 = "align2-" + token dsk2 = { (name2, i): (getitem, key, 1) for i, key in enumerate(aligned.__dask_keys__()) } dsk2.update(aligned.dask) result2 = new_dd_object(dsk2, name2, meta2, aligned.divisions) return result1, result2 @derived_from(pd.DataFrame) def combine(self, other, func, fill_value=None, overwrite=True): return self.map_partitions( M.combine, other, func, fill_value=fill_value, overwrite=overwrite ) @derived_from(pd.DataFrame) def combine_first(self, other): return self.map_partitions(M.combine_first, other) @classmethod def _bind_operator_method(cls, name, op, original=pd.DataFrame): """bind operator method like DataFrame.add to this class""" raise NotImplementedError @derived_from(pd.DataFrame) def resample(self, rule, closed=None, label=None): from dask.dataframe.tseries.resample import Resampler return Resampler(self, rule, closed=closed, label=label) @_deprecated( message=( "Will be removed in a future version. " "Please create a mask and filter using .loc instead" ) ) @derived_from(pd.DataFrame) def first(self, offset): # Let pandas error on bad args self._meta_nonempty.first(offset) if not self.known_divisions: raise ValueError("`first` is not implemented for unknown divisions") offset = pd.tseries.frequencies.to_offset(offset) date = self.divisions[0] + offset end = self.loc._get_partitions(date) is_anchored = offset.is_anchored() include_right = is_anchored or not hasattr(offset, "delta") if end == self.npartitions - 1: divs = self.divisions else: divs = self.divisions[: end + 1] + (date,) name = "first-" + tokenize(self, offset) dsk = {(name, i): (self._name, i) for i in range(end)} dsk[(name, end)] = ( methods.boundary_slice, (self._name, end), None, date, include_right, True, ) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, self, divs) @_deprecated( message=( "Will be removed in a future version. " "Please create a mask and filter using .loc instead" ) ) @derived_from(pd.DataFrame) def last(self, offset): # Let pandas error on bad args self._meta_nonempty.last(offset) if not self.known_divisions: raise ValueError("`last` is not implemented for unknown divisions") offset = pd.tseries.frequencies.to_offset(offset) date = self.divisions[-1] - offset start = self.loc._get_partitions(date) if start == 0: divs = self.divisions else: divs = (date,) + self.divisions[start + 1 :] name = "last-" + tokenize(self, offset) dsk = { (name, i + 1): (self._name, j + 1) for i, j in enumerate(range(start, self.npartitions)) } dsk[(name, 0)] = ( methods.boundary_slice, (self._name, start), date, None, True, False, ) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, self, divs) def nunique_approx(self, split_every=None): """Approximate number of unique rows. This method uses the HyperLogLog algorithm for cardinality estimation to compute the approximate number of unique rows. The approximate error is 0.406%. Parameters ---------- split_every : int, optional Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used. Default is 8. Returns ------- a float representing the approximate number of elements """ from dask.dataframe import hyperloglog # here to avoid circular import issues return aca( [self], chunk=hyperloglog.compute_hll_array, combine=hyperloglog.reduce_state, aggregate=hyperloglog.estimate_count, split_every=split_every, b=16, meta=float, ) @property def values(self): """Return a dask.array of the values of this dataframe Warning: This creates a dask.array without precise shape information. Operations that depend on shape information, like slicing or reshaping, will not work. """ if is_extension_array_dtype(self._meta.values): warnings.warn( "Dask currently has limited support for converting pandas extension dtypes " f"to arrays. Converting {self._meta.values.dtype} to object dtype.", UserWarning, ) return self.map_partitions(methods.values) def _validate_chunks(self, arr, lengths): from dask.array.core import normalize_chunks if isinstance(lengths, Sequence): lengths = tuple(lengths) if len(lengths) != self.npartitions: raise ValueError( "The number of items in 'lengths' does not match the number of " f"partitions. {len(lengths)} != {self.npartitions}" ) if self.ndim == 1: chunks = normalize_chunks((lengths,)) else: chunks = normalize_chunks((lengths, (len(self.columns),))) return chunks elif lengths is not None: raise ValueError(f"Unexpected value for 'lengths': '{lengths}'") return arr._chunks def _is_index_level_reference(self, key): """ Test whether a key is an index level reference To be considered an index level reference, `key` must match the index name and must NOT match the name of any column (if a dataframe). """ return ( self.index.name is not None and not is_dask_collection(key) and (np.isscalar(key) or isinstance(key, tuple)) and key == self.index.name and key not in getattr(self, "columns", ()) ) def _contains_index_name(self, columns_or_index): """ Test whether the input contains a reference to the index of the DataFrame/Series """ if isinstance(columns_or_index, list): return any(self._is_index_level_reference(n) for n in columns_or_index) else: return self._is_index_level_reference(columns_or_index) def _raise_if_object_series(x, funcname): """ Utility function to raise an error if an object column does not support a certain operation like `mean`. """ if isinstance(x, Series) and hasattr(x, "dtype"): if x.dtype == object: raise ValueError("`%s` not supported with object series" % funcname) elif is_string_dtype(x): raise ValueError("`%s` not supported with string series" % funcname) class Series(_Frame): """Parallel Pandas Series Do not use this class directly. Instead use functions like ``dd.read_csv``, ``dd.read_parquet``, or ``dd.from_pandas``. Parameters ---------- dsk: dict The dask graph to compute this Series _name: str The key prefix that specifies which keys in the dask comprise this particular Series meta: pandas.Series An empty ``pandas.Series`` with names, dtypes, and index matching the expected output. divisions: tuple of index values Values along which we partition our blocks on the index See Also -------- dask.dataframe.DataFrame """ _partition_type = pd.Series _is_partition_type = staticmethod(is_series_like) _token_prefix = "series-" _accessors: ClassVar[set[str]] = set() def __array_wrap__(self, array, context=None): if isinstance(context, tuple) and len(context) > 0: if isinstance(context[1][0], np.ndarray) and context[1][0].shape == (): index = None else: index = context[1][0].index else: try: import inspect method_name = f"`{inspect.stack()[3][3]}`" except IndexError: method_name = "This method" raise NotImplementedError( f"{method_name} is not implemented for `dask.dataframe.Series`." ) return meta_series_constructor(self)(array, index=index, name=self.name) @property def axes(self): return [self.index] @property def name(self): return self._meta.name @name.setter def name(self, name): self._meta.name = name renamed = _rename_dask(self, name) # update myself self.dask = renamed.dask self._name = renamed._name @property def ndim(self): """Return dimensionality""" return 1 @property def shape(self): """ Return a tuple representing the dimensionality of a Series. The single element of the tuple is a Delayed result. Examples -------- >>> series.shape # doctest: +SKIP (dd.Scalar,) """ return (self.size,) @property def dtype(self): """Return data type""" return self._meta.dtype dt = CachedAccessor("dt", DatetimeAccessor) cat = CachedAccessor("cat", CategoricalAccessor) str = CachedAccessor("str", StringAccessor) def __dir__(self): o = set(dir(type(self))) o.update(self.__dict__) # Remove the `cat` and `str` accessors if not available. We can't # decide this statically for the `dt` accessor, as it works on # datetime-like things as well. for accessor in ["cat", "str"]: if not hasattr(self._meta, accessor): o.remove(accessor) return list(o) @property def nbytes(self): """Number of bytes""" return self.reduction( methods.nbytes, np.sum, token="nbytes", meta=int, split_every=False ) def _repr_data(self): return _repr_data_series(self._meta, self._repr_divisions) def __repr__(self): """have to overwrite footer""" if self.name is not None: footer = f"Name: {self.name}, dtype: {self.dtype}" else: footer = f"dtype: {self.dtype}" return """Dask {klass} Structure: {data} {footer} Dask Name: {name}, {layers}""".format( klass=self.__class__.__name__, data=self.to_string(), footer=footer, name=key_split(self._name), layers=maybe_pluralize(len(self.dask.layers), "graph layer"), ) def rename(self, index=None, inplace=no_default, sorted_index=False): """Alter Series index labels or name Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar value. Parameters ---------- index : scalar, hashable sequence, dict-like or callable, optional If dict-like or callable, the transformation is applied to the index. Scalar or hashable sequence-like will alter the ``Series.name`` attribute. inplace : boolean, default False Whether to return a new Series or modify this one inplace. sorted_index : bool, default False If true, the output ``Series`` will have known divisions inferred from the input series and the transformation. Ignored for non-callable/dict-like ``index`` or when the input series has unknown divisions. Note that this may only be set to ``True`` if you know that the transformed index is monotonically increasing. Dask will check that transformed divisions are monotonic, but cannot check all the values between divisions, so incorrectly setting this can result in bugs. Returns ------- renamed : Series See Also -------- pandas.Series.rename """ from pandas.api.types import is_dict_like, is_list_like, is_scalar import dask.dataframe as dd if inplace is not no_default: warnings.warn( "'inplace' argument for dask series will be removed in future versions", FutureWarning, ) else: inplace = False if is_scalar(index) or ( is_list_like(index) and not is_dict_like(index) and not isinstance(index, dd.Series) ): res = self if inplace else self.copy() res.name = index else: res = self.map_partitions(M.rename, index, enforce_metadata=False) if self.known_divisions: if sorted_index and (callable(index) or is_dict_like(index)): old = pd.Series(range(self.npartitions + 1), index=self.divisions) new = old.rename(index).index if not new.is_monotonic_increasing: msg = ( "sorted_index=True, but the transformed index " "isn't monotonic_increasing" ) raise ValueError(msg) res._divisions = tuple(methods.tolist(new)) else: res = res.clear_divisions() if inplace: self.dask = res.dask self._name = res._name self._divisions = res.divisions self._meta = res._meta res = self return res @derived_from(pd.Series) def round(self, decimals=0): return elemwise(M.round, self, decimals) @derived_from(pd.Series) def to_timestamp(self, freq=None, how="start", axis=0): df = elemwise(M.to_timestamp, self, freq, how, axis) df.divisions = tuple(pd.Index(self.divisions).to_timestamp(freq=freq, how=how)) return df def quantile(self, q=0.5, method="default"): """Approximate quantiles of Series Parameters ---------- q : list/array of floats, default 0.5 (50%) Iterable of numbers ranging from 0 to 1 for the desired quantiles method : {'default', 'tdigest', 'dask'}, optional What method to use. By default will use dask's internal custom algorithm (``'dask'``). If set to ``'tdigest'`` will use tdigest for floats and ints and fallback to the ``'dask'`` otherwise. """ return quantile(self, q, method=method) def median_approximate(self, method="default"): """Return the approximate median of the values over the requested axis. Parameters ---------- method : {'default', 'tdigest', 'dask'}, optional What method to use. By default will use Dask's internal custom algorithm (``"dask"``). If set to ``"tdigest"`` will use tdigest for floats and ints and fallback to the ``"dask"`` otherwise. """ return self.quantile(q=0.5, method=method) @derived_from(pd.Series) def median(self, method="default"): if self.npartitions == 1: # Can provide an exact median in these cases return self.median_approximate(method=method) raise NotImplementedError( "Dask doesn't implement an exact median in all cases as this is hard to do in parallel. " "See the `median_approximate` method instead, which uses an approximate algorithm." ) def _repartition_quantiles(self, npartitions, upsample=1.0): """Approximate quantiles of Series used for repartitioning""" from dask.dataframe.partitionquantiles import partition_quantiles return partition_quantiles(self, npartitions, upsample=upsample) def __getitem__(self, key): if isinstance(key, Series) and self.divisions == key.divisions: name = "index-%s" % tokenize(self, key) dsk = partitionwise_graph(operator.getitem, name, self, key) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self, key]) return Series(graph, name, self._meta, self.divisions) return self.loc[key] @derived_from(pd.DataFrame) def _get_numeric_data(self, how="any", subset=None): return self if not PANDAS_GE_200: @derived_from(pd.Series) def iteritems(self): if PANDAS_GE_150: warnings.warn( "iteritems is deprecated and will be removed in a future version. " "Use .items instead.", FutureWarning, ) # We use the `_` generator below to ensure the deprecation warning above # is raised when `.iteritems()` is called, not when the first `next()` # iteration happens def _(self): for i in range(self.npartitions): s = self.get_partition(i).compute() yield from s.items() return _(self) @derived_from(pd.Series) def __iter__(self): for i in range(self.npartitions): s = self.get_partition(i).compute() yield from s @_deprecated( message=( "Using the ``in`` operator to test for membership in Series is " "deprecated. To test for membership in the index use " "``(s.index == key).any()``. Similarly to test for membership in " "the values use ``(s == key).any()``" ) ) def __contains__(self, key): return (self == key).any().compute() @classmethod def _validate_axis(cls, axis=0, none_is_zero: bool = True) -> None | Literal[0, 1]: if axis not in (0, "index", None): raise ValueError(f"No axis named {axis}") # convert to numeric axis numeric_axis: dict[str | None, Literal[0, 1]] = {"index": 0} if none_is_zero: numeric_axis[None] = 0 return numeric_axis.get(axis, axis) @derived_from(pd.Series) def groupby( self, by=None, group_keys=GROUP_KEYS_DEFAULT, sort=None, observed=None, dropna=None, **kwargs, ): from dask.dataframe.groupby import SeriesGroupBy return SeriesGroupBy( self, by=by, group_keys=group_keys, sort=sort, observed=observed, dropna=dropna, **kwargs, ) @derived_from(pd.Series) def count(self, split_every=False): return super().count(split_every=split_every) @derived_from(pd.Series) def mode(self, dropna=True, split_every=False): return super().mode(dropna=dropna, split_every=split_every) @derived_from(pd.Series) def explode(self): meta = self._meta.explode() return self.map_partitions(M.explode, meta=meta, enforce_metadata=False) def unique(self, split_every=None, split_out=1): """ Return Series of unique values in the object. Includes NA values. Returns ------- uniques : Series """ return aca( self, chunk=methods.unique, aggregate=methods.unique, meta=self._meta, token="unique", split_every=split_every, series_name=self.name, split_out=split_out, ) @derived_from(pd.Series) def nunique(self, split_every=None, dropna=True): uniqs = self.drop_duplicates(split_every=split_every) if dropna: # count mimics pandas behavior and excludes NA values return uniqs.count() else: return uniqs.size @derived_from(pd.Series) def value_counts( self, sort=None, ascending=False, dropna=True, normalize=False, split_every=None, split_out=1, ): kwargs = {"sort": sort, "ascending": ascending, "dropna": dropna} aggregate_kwargs = {"normalize": normalize} if split_out > 1: aggregate_kwargs["total_length"] = ( len(self) if dropna is False else len(self.dropna()) ) return aca( self, chunk=M.value_counts, aggregate=methods.value_counts_aggregate, combine=methods.value_counts_combine, meta=self._meta.value_counts(normalize=normalize), token="value-counts", split_every=split_every, split_out=split_out, split_out_setup=split_out_on_index, aggregate_kwargs=aggregate_kwargs, **kwargs, ) @derived_from(pd.Series) def nlargest(self, n=5, split_every=None): return aca( self, chunk=M.nlargest, aggregate=M.nlargest, meta=self._meta, token="series-nlargest", split_every=split_every, n=n, ) @derived_from(pd.Series) def nsmallest(self, n=5, split_every=None): return aca( self, chunk=M.nsmallest, aggregate=M.nsmallest, meta=self._meta, token="series-nsmallest", split_every=split_every, n=n, ) @derived_from(pd.Series) def isin(self, values): # Added just to get the different docstring for Series return super().isin(values) @insert_meta_param_description(pad=12) @derived_from(pd.Series) def map(self, arg, na_action=None, meta=no_default): if is_series_like(arg) and is_dask_collection(arg): return series_map(self, arg) if not ( isinstance(arg, dict) or callable(arg) or is_series_like(arg) and not is_dask_collection(arg) ): raise TypeError( f"arg must be pandas.Series, dict or callable. Got {type(arg)}" ) name = "map-" + tokenize(self, arg, na_action) dsk = { (name, i): (M.map, k, arg, na_action) for i, k in enumerate(self.__dask_keys__()) } graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) if meta is no_default: meta = _emulate(M.map, self, arg, na_action=na_action, udf=True) else: meta = make_meta( meta, index=getattr(make_meta(self), "index", None), parent_meta=self._meta, ) return type(self)(graph, name, meta, self.divisions) @derived_from(pd.Series) def dropna(self): return self.map_partitions(M.dropna, enforce_metadata=False) @derived_from(pd.Series) def between(self, left, right, inclusive="both"): return self.map_partitions( M.between, left=left, right=right, inclusive=inclusive ) @_dummy_numpy_dispatcher("out") @derived_from(pd.Series) def clip(self, lower=None, upper=None, axis=None): if axis not in (None, 0): raise ValueError(f"Series.clip does not support axis={axis}") # np.clip may pass out return self.map_partitions( M.clip, lower=lower, upper=upper, enforce_metadata=False, # See: https://github.com/rapidsai/cudf/issues/14369 **({} if axis is None else {"axis": axis}), ) @derived_from(pd.Series) def align(self, other, join="outer", axis=None, fill_value=None): return super().align(other, join=join, axis=axis, fill_value=fill_value) @derived_from(pd.Series) def combine(self, other, func, fill_value=None): return self.map_partitions(M.combine, other, func, fill_value=fill_value) @derived_from(pd.Series) def squeeze(self): return self @derived_from(pd.Series) def combine_first(self, other): return self.map_partitions(M.combine_first, other) def to_bag(self, index=False, format="tuple"): """Create a Dask Bag from a Series""" from dask.dataframe.io import to_bag return to_bag(self, index, format=format) @derived_from(pd.Series) def to_frame(self, name=None): args = [] if name is None else [name] return self.map_partitions(M.to_frame, *args, meta=self._meta.to_frame(*args)) @derived_from(pd.Series) def to_string(self, max_rows=5): # option_context doesn't affect return self._repr_data().to_string(max_rows=max_rows) @classmethod def _bind_operator_method(cls, name, op, original=pd.Series): """bind operator method like Series.add to this class""" def meth(self, other, level=None, fill_value=None, axis=0): if level is not None: raise NotImplementedError("level must be None") axis = self._validate_axis(axis) meta = _emulate(op, self, other, axis=axis, fill_value=fill_value) return map_partitions( op, self, other, meta=meta, axis=axis, fill_value=fill_value ) meth.__name__ = name setattr(cls, name, derived_from(original)(meth)) @classmethod def _bind_comparison_method(cls, name, comparison, original=pd.Series): """bind comparison method like Series.eq to this class""" def meth(self, other, level=None, fill_value=None, axis=0): if level is not None: raise NotImplementedError("level must be None") axis = self._validate_axis(axis) if fill_value is None: return elemwise(comparison, self, other, axis=axis) else: op = partial(comparison, fill_value=fill_value) return elemwise(op, self, other, axis=axis) meth.__name__ = name setattr(cls, name, derived_from(original)(meth)) @insert_meta_param_description(pad=12) def apply(self, func, convert_dtype=no_default, meta=no_default, args=(), **kwds): """Parallel version of pandas.Series.apply Parameters ---------- func : function Function to apply convert_dtype : boolean, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object. $META args : tuple Positional arguments to pass to function in addition to the value. Additional keyword arguments will be passed as keywords to the function. Returns ------- applied : Series or DataFrame if func returns a Series. Examples -------- >>> import dask.dataframe as dd >>> s = pd.Series(range(5), name='x') >>> ds = dd.from_pandas(s, npartitions=2) Apply a function elementwise across the Series, passing in extra arguments in ``args`` and ``kwargs``: >>> def myadd(x, a, b=1): ... return x + a + b >>> res = ds.apply(myadd, args=(2,), b=1.5) # doctest: +SKIP By default, dask tries to infer the output metadata by running your provided function on some fake data. This works well in many cases, but can sometimes be expensive, or even fail. To avoid this, you can manually specify the output metadata with the ``meta`` keyword. This can be specified in many forms, for more information see ``dask.dataframe.utils.make_meta``. Here we specify the output is a Series with name ``'x'``, and dtype ``float64``: >>> res = ds.apply(myadd, args=(2,), b=1.5, meta=('x', 'f8')) In the case where the metadata doesn't change, you can also pass in the object itself directly: >>> res = ds.apply(lambda x: x + 1, meta=ds) See Also -------- dask.Series.map_partitions """ if convert_dtype is not no_default: kwds["convert_dtype"] = convert_dtype # let pandas trigger any warnings, such as convert_dtype warning self._meta.apply(func, args=args, **kwds) if meta is no_default: with check_convert_dtype_deprecation(): meta = _emulate( M.apply, self._meta_nonempty, func, args=args, udf=True, **kwds ) warnings.warn(meta_warning(meta)) return map_partitions(methods.apply, self, func, args=args, meta=meta, **kwds) @derived_from(pd.Series) def cov(self, other, min_periods=None, split_every=False): from dask.dataframe.multi import concat if not isinstance(other, Series): raise TypeError("other must be a dask.dataframe.Series") df = concat([self, other], axis=1) return _cov_corr( df, min_periods, scalar=True, numeric_only=False, split_every=split_every ) @derived_from(pd.Series) def corr(self, other, method="pearson", min_periods=None, split_every=False): from dask.dataframe.multi import concat if not isinstance(other, Series): raise TypeError("other must be a dask.dataframe.Series") if method != "pearson": raise NotImplementedError("Only Pearson correlation has been implemented") df = concat([self, other], axis=1) return _cov_corr( df, min_periods, corr=True, scalar=True, numeric_only=False, split_every=split_every, ) @derived_from(pd.Series) def autocorr(self, lag=1, split_every=False): if not isinstance(lag, Integral): raise TypeError("lag must be an integer") return self.corr(self if lag == 0 else self.shift(lag), split_every=split_every) @derived_from(pd.Series) def memory_usage(self, index=True, deep=False): return self.reduction( M.memory_usage, M.sum, chunk_kwargs={"index": index, "deep": deep}, split_every=False, token=self._token_prefix + "memory-usage", ) def __divmod__(self, other): res1 = self // other res2 = self % other return res1, res2 def __rdivmod__(self, other): res1 = other // self res2 = other % self return res1, res2 if not PANDAS_GE_200: @property @derived_from(pd.Series) def is_monotonic(self): if PANDAS_GE_150: warnings.warn( "is_monotonic is deprecated and will be removed in a future version. " "Use is_monotonic_increasing instead.", FutureWarning, ) return self.is_monotonic_increasing @property @derived_from(pd.Series) def is_monotonic_increasing(self): return aca( self, chunk=methods.monotonic_increasing_chunk, combine=methods.monotonic_increasing_combine, aggregate=methods.monotonic_increasing_aggregate, meta=bool, token="monotonic_increasing", ) @property @derived_from(pd.Series) def is_monotonic_decreasing(self): return aca( self, chunk=methods.monotonic_decreasing_chunk, combine=methods.monotonic_decreasing_combine, aggregate=methods.monotonic_decreasing_aggregate, meta=bool, token="monotonic_decreasing", ) @_deprecated( message=( "Will be removed in a future version. " "Use `Series.astype()` as an alternative to change the dtype." ) ) @derived_from(pd.Series) def view(self, dtype): return self._view(dtype) def _view(self, dtype): meta = self._meta.view(dtype) return self.map_partitions(M.view, dtype, meta=meta) class Index(Series): _partition_type = pd.Index _is_partition_type = staticmethod(is_index_like) _token_prefix = "index-" _accessors: ClassVar[set[str]] = set() _dt_attributes = { "nanosecond", "microsecond", "millisecond", "dayofyear", "minute", "hour", "day", "dayofweek", "second", "week", "weekday", "weekofyear", "month", "quarter", "year", } _cat_attributes = { "known", "as_known", "as_unknown", "add_categories", "categories", "remove_categories", "reorder_categories", "as_ordered", "codes", "remove_unused_categories", "set_categories", "as_unordered", "ordered", "rename_categories", } _monotonic_attributes = { "is_monotonic", "is_monotonic_increasing", "is_monotonic_decreasing", } def __getattr__(self, key): if ( isinstance(self._meta.dtype, pd.CategoricalDtype) and key in self._cat_attributes ): return getattr(self.cat, key) elif key in self._dt_attributes: return getattr(self.dt, key) elif key in self._monotonic_attributes: return getattr(self, key) raise AttributeError("'Index' object has no attribute %r" % key) def __dir__(self): out = super().__dir__() out.extend(self._dt_attributes) if isinstance(self.dtype, pd.CategoricalDtype): out.extend(self._cat_attributes) return out # Typing: https://github.com/python/mypy/issues/4125 @property # type: ignore def index(self): raise AttributeError( f"{self.__class__.__name__!r} object has no attribute 'index'" ) def __array_wrap__(self, array, context=None): return pd.Index(array, name=self.name) def head(self, n=5, compute=True): """First n items of the Index. Caveat, this only checks the first partition. """ name = "head-%d-%s" % (n, self._name) dsk = {(name, 0): (operator.getitem, (self._name, 0), slice(0, n))} graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) result = new_dd_object(graph, name, self._meta, self.divisions[:2]) if compute: result = result.compute() return result @derived_from(pd.Index) def max(self, split_every=False): return self.reduction( M.max, meta=self._meta_nonempty.max(), token=self._token_prefix + "max", split_every=split_every, ) @derived_from(pd.Index) def min(self, split_every=False): return self.reduction( M.min, meta=self._meta_nonempty.min(), token=self._token_prefix + "min", split_every=split_every, ) def count(self, split_every=False): return self.reduction( methods.index_count, np.sum, token="index-count", meta=int, split_every=split_every, ) @derived_from(pd.Index) def shift(self, periods=1, freq=None): if isinstance(self._meta, pd.PeriodIndex): if freq is not None: raise ValueError("PeriodIndex doesn't accept `freq` argument") meta = self._meta_nonempty.shift(periods) out = self.map_partitions( M.shift, periods, meta=meta, token="shift", transform_divisions=False ) else: # Pandas will raise for other index types that don't implement shift meta = self._meta_nonempty.shift(periods, freq=freq) out = self.map_partitions( M.shift, periods, token="shift", meta=meta, freq=freq, transform_divisions=False, ) if freq is None: freq = meta.freq return maybe_shift_divisions(out, periods, freq=freq) @derived_from(pd.Index) def to_series(self): return self.map_partitions( M.to_series, meta=self._meta.to_series(), transform_divisions=False, ) @derived_from(pd.Index, ua_args=["index"]) def to_frame(self, index=True, name=None): if not index: raise NotImplementedError() args = [index] if name is None else [index, name] return self.map_partitions( M.to_frame, *args, meta=self._meta.to_frame(*args), transform_divisions=False, ) @insert_meta_param_description(pad=12) @derived_from(pd.Index) def map(self, arg, na_action=None, meta=no_default, is_monotonic=False): """ Note that this method clears any known divisions. If your mapping function is monotonically increasing then use `is_monotonic` to apply the mapping function to the old divisions and assign the new divisions to the output. """ applied = super().map(arg, na_action=na_action, meta=meta) if is_monotonic and self.known_divisions: applied.divisions = tuple( pd.Series(self.divisions).map(arg, na_action=na_action) ) else: applied = applied.clear_divisions() return applied if not PANDAS_GE_200: @property @derived_from(pd.Index) def is_monotonic(self): if PANDAS_GE_150: warnings.warn( "is_monotonic is deprecated and will be removed in a future version. " "Use is_monotonic_increasing instead.", FutureWarning, ) return super().is_monotonic_increasing @property @derived_from(pd.Index) def is_monotonic_increasing(self): return super().is_monotonic_increasing @property @derived_from(pd.Index) def is_monotonic_decreasing(self): return super().is_monotonic_decreasing @derived_from(pd.Index) def memory_usage(self, deep=False): return self.reduction( M.memory_usage, M.sum, chunk_kwargs={"deep": deep}, split_every=False, token=self._token_prefix + "memory-usage", ) @_deprecated_kwarg("shuffle", "shuffle_method") @derived_from( pd.Index, inconsistencies="keep=False will raise a ``NotImplementedError``", ) def drop_duplicates( self, split_every=None, split_out=1, shuffle_method=None, **kwargs, ): if not self.known_divisions: # Use base class if we have unknown divisions return super().drop_duplicates( split_every=split_every, split_out=split_out, shuffle_method=shuffle_method, **kwargs, ) # Let pandas error on bad inputs self._meta_nonempty.drop_duplicates(**kwargs) # Raise error for unsupported `keep` if kwargs.get("keep", True) is False: raise NotImplementedError("drop_duplicates with keep=False") # Simple `drop_duplicates` case that we are acting on # an Index with known divisions chunk = M.drop_duplicates repartition_npartitions = max( self.npartitions // (split_every or self.npartitions), split_out, ) assert self.known_divisions, "Requires known divisions" return ( self.map_partitions( chunk, token="drop-duplicates-chunk", meta=self._meta, transform_divisions=False, **kwargs, ) .repartition(npartitions=repartition_npartitions) .map_partitions( chunk, token="drop-duplicates-agg", meta=self._meta, transform_divisions=False, **kwargs, ) .repartition(npartitions=split_out) ) class DataFrame(_Frame): """ Parallel Pandas DataFrame Do not use this class directly. Instead use functions like ``dd.read_csv``, ``dd.read_parquet``, or ``dd.from_pandas``. Parameters ---------- dsk: dict The dask graph to compute this DataFrame name: str The key prefix that specifies which keys in the dask comprise this particular DataFrame meta: pandas.DataFrame An empty ``pandas.DataFrame`` with names, dtypes, and index matching the expected output. divisions: tuple of index values Values along which we partition our blocks on the index """ _partition_type = pd.DataFrame _is_partition_type = staticmethod(is_dataframe_like) _token_prefix = "dataframe-" _accessors: ClassVar[set[str]] = set() def __init__(self, dsk, name, meta, divisions): super().__init__(dsk, name, meta, divisions) if self.dask.layers[name].collection_annotations is None: self.dask.layers[name].collection_annotations = { "npartitions": self.npartitions, "columns": [col for col in self.columns], "type": typename(type(self)), "dataframe_type": typename(type(self._meta)), "series_dtypes": { col: self._meta[col].dtype if hasattr(self._meta[col], "dtype") else None for col in self._meta.columns }, } else: self.dask.layers[name].collection_annotations.update( { "npartitions": self.npartitions, "columns": [col for col in self.columns], "type": typename(type(self)), "dataframe_type": typename(type(self._meta)), "series_dtypes": { col: self._meta[col].dtype if hasattr(self._meta[col], "dtype") else None for col in self._meta.columns }, } ) def __array_wrap__(self, array, context=None): if isinstance(context, tuple) and len(context) > 0: if isinstance(context[1][0], np.ndarray) and context[1][0].shape == (): index = None else: index = context[1][0].index else: try: import inspect method_name = f"`{inspect.stack()[3][3]}`" except IndexError: method_name = "This method" raise NotImplementedError( f"{method_name} is not implemented for `dask.dataframe.DataFrame`." ) return meta_frame_constructor(self)(array, index=index, columns=self.columns) @property def axes(self): return [self.index, self.columns] @property def columns(self): return self._meta.columns @columns.setter def columns(self, columns): renamed = _rename_dask(self, columns) self._meta = renamed._meta self._name = renamed._name self.dask = renamed.dask @property def iloc(self): """Purely integer-location based indexing for selection by position. Only indexing the column positions is supported. Trying to select row positions will raise a ValueError. See :ref:`dataframe.indexing` for more. Examples -------- >>> df.iloc[:, [2, 0, 1]] # doctest: +SKIP """ from dask.dataframe.indexing import _iLocIndexer # For dataframes with unique column names, this will be transformed into a __getitem__ call return _iLocIndexer(self) def __len__(self): try: s = self.iloc[:, 0] except IndexError: return super().__len__() else: return len(s) def __contains__(self, key): return key in self._meta @property def empty(self): # __getattr__ will be called after we raise this, so we'll raise it again from there raise AttributeNotImplementedError( "Checking whether a Dask DataFrame has any rows may be expensive. " "However, checking the number of columns is fast. " "Depending on which of these results you need, use either " "`len(df.index) == 0` or `len(df.columns) == 0`" ) def __getitem__(self, key): name = "getitem-%s" % tokenize(self, key) if np.isscalar(key) or isinstance(key, (tuple, str)): if isinstance(self._meta.index, (pd.DatetimeIndex, pd.PeriodIndex)): if key not in self._meta.columns: warnings.warn( "Indexing a DataFrame with a datetimelike index using a single " "string to slice the rows, like `frame[string]`, is deprecated " "and will be removed in a future version. Use `frame.loc[string]` " "instead.", FutureWarning, ) return self.loc[key] # error is raised from pandas meta = self._meta[_extract_meta(key)] dsk = partitionwise_graph(operator.getitem, name, self, key) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, meta, self.divisions) elif isinstance(key, slice): from pandas.api.types import is_float_dtype is_integer_slice = any( isinstance(i, Integral) for i in (key.start, key.step, key.stop) ) # Slicing with integer labels is always iloc based except for a # float indexer for some reason if is_integer_slice and not is_float_dtype(self.index.dtype): # NOTE: this always fails currently, as iloc is mostly # unsupported, but we call it anyway here for future-proofing # and error-attribution purposes return self.iloc[key] else: return self.loc[key] if isinstance(key, (np.ndarray, list)) or ( not is_dask_collection(key) and (is_series_like(key) or is_index_like(key)) ): # error is raised from pandas meta = self._meta[_extract_meta(key)] dsk = partitionwise_graph(operator.getitem, name, self, key) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) return new_dd_object(graph, name, meta, self.divisions) if isinstance(key, Series): # do not perform dummy calculation, as columns will not be changed. if self.divisions != key.divisions: from dask.dataframe.multi import _maybe_align_partitions self, key = _maybe_align_partitions([self, key]) dsk = partitionwise_graph(operator.getitem, name, self, key) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self, key]) return new_dd_object(graph, name, self, self.divisions) if isinstance(key, DataFrame): return self.where(key, np.nan) raise NotImplementedError(key) def __setitem__(self, key, value): if isinstance(key, (tuple, list)) and isinstance(value, DataFrame): df = self.assign(**{k: value[c] for k, c in zip(key, value.columns)}) elif isinstance(key, pd.Index) and not isinstance(value, DataFrame): key = list(key) df = self.assign(**{k: value for k in key}) elif ( is_dataframe_like(key) or is_series_like(key) or isinstance(key, (DataFrame, Series)) ): df = self.where(~key, value) elif not isinstance(key, str): raise NotImplementedError(f"Item assignment with {type(key)} not supported") else: df = self.assign(**{key: value}) self.dask = df.dask self._name = df._name self._meta = df._meta self._divisions = df.divisions def __delitem__(self, key): result = self.drop([key], axis=1) self.dask = result.dask self._name = result._name self._meta = result._meta def __setattr__(self, key, value): try: columns = object.__getattribute__(self, "_meta").columns except AttributeError: columns = () # exclude protected attributes from setitem if key in columns and key not in ["divisions", "dask", "_name", "_meta"]: self[key] = value else: object.__setattr__(self, key, value) def __getattr__(self, key): if key in self.columns: return self[key] elif key == "empty": # self.empty raises AttributeNotImplementedError, which includes # AttributeError, which means we end up here in self.__getattr__, # because DataFrame.__getattribute__ doesn't think the attribute exists # and uses __getattr__ as the fallback. So, get `self.empty` more # forcefully via object.__getattribute__ to raise informative error. object.__getattribute__(self, key) else: raise AttributeError("'DataFrame' object has no attribute %r" % key) def __dir__(self): o = set(dir(type(self))) o.update(self.__dict__) o.update(c for c in self.columns if (isinstance(c, str) and c.isidentifier())) return list(o) def __iter__(self): return iter(self._meta) def _ipython_key_completions_(self): return methods.tolist(self.columns) @property def ndim(self): """Return dimensionality""" return 2 @property def shape(self): """ Return a tuple representing the dimensionality of the DataFrame. The number of rows is a Delayed result. The number of columns is a concrete integer. Examples -------- >>> df.size # doctest: +SKIP (Delayed('int-07f06075-5ecc-4d77-817e-63c69a9188a8'), 2) """ col_size = len(self.columns) if col_size == 0: return (self.index.shape[0], 0) row_size = delayed(int)(self.size / col_size) return (row_size, col_size) @property def dtypes(self): """Return data types""" return self._meta.dtypes @derived_from(pd.DataFrame) def select_dtypes(self, include=None, exclude=None): cs = self._meta.select_dtypes(include=include, exclude=exclude) indexer = self._get_columns_indexes_based_on_dtypes(cs) return self.iloc[:, indexer] def sort_values( self, by: str | list[str], npartitions: int | Literal["auto"] | None = None, ascending: bool = True, na_position: Literal["first"] | Literal["last"] = "last", sort_function: Callable[[pd.DataFrame], pd.DataFrame] | None = None, sort_function_kwargs: Mapping[str, Any] | None = None, **kwargs, ) -> DataFrame: """Sort the dataset by a single column. Sorting a parallel dataset requires expensive shuffles and is generally not recommended. See ``set_index`` for implementation details. Parameters ---------- by: str or list[str] Column(s) to sort by. npartitions: int, None, or 'auto' The ideal number of output partitions. If None, use the same as the input. If 'auto' then decide by memory use. ascending: bool, optional Sort ascending vs. descending. Defaults to True. na_position: {'last', 'first'}, optional Puts NaNs at the beginning if 'first', puts NaN at the end if 'last'. Defaults to 'last'. sort_function: function, optional Sorting function to use when sorting underlying partitions. If None, defaults to ``M.sort_values`` (the partition library's implementation of ``sort_values``). sort_function_kwargs: dict, optional Additional keyword arguments to pass to the partition sorting function. By default, ``by``, ``ascending``, and ``na_position`` are provided. Examples -------- >>> df2 = df.sort_values('x') # doctest: +SKIP """ from dask.dataframe.shuffle import sort_values return sort_values( self, by, ascending=ascending, npartitions=npartitions, na_position=na_position, sort_function=sort_function, sort_function_kwargs=sort_function_kwargs, **kwargs, ) def set_index( self, other: str | Series, drop: bool = True, sorted: bool = False, npartitions: int | Literal["auto"] | None = None, divisions: Sequence | None = None, inplace: bool = False, sort: bool = True, **kwargs, ): """Set the DataFrame index (row labels) using an existing column. If ``sort=False``, this function operates exactly like ``pandas.set_index`` and sets the index on the DataFrame. If ``sort=True`` (default), this function also sorts the DataFrame by the new index. This can have a significant impact on performance, because joins, groupbys, lookups, etc. are all much faster on that column. However, this performance increase comes with a cost, sorting a parallel dataset requires expensive shuffles. Often we ``set_index`` once directly after data ingest and filtering and then perform many cheap computations off of the sorted dataset. With ``sort=True``, this function is much more expensive. Under normal operation this function does an initial pass over the index column to compute approximate quantiles to serve as future divisions. It then passes over the data a second time, splitting up each input partition into several pieces and sharing those pieces to all of the output partitions now in sorted order. In some cases we can alleviate those costs, for example if your dataset is sorted already then we can avoid making many small pieces or if you know good values to split the new index column then we can avoid the initial pass over the data. For example if your new index is a datetime index and your data is already sorted by day then this entire operation can be done for free. You can control these options with the following parameters. Parameters ---------- other: string or Dask Series Column to use as index. drop: boolean, default True Delete column to be used as the new index. sorted: bool, optional If the index column is already sorted in increasing order. Defaults to False npartitions: int, None, or 'auto' The ideal number of output partitions. If None, use the same as the input. If 'auto' then decide by memory use. Only used when ``divisions`` is not given. If ``divisions`` is given, the number of output partitions will be ``len(divisions) - 1``. divisions: list, optional The "dividing lines" used to split the new index into partitions. For ``divisions=[0, 10, 50, 100]``, there would be three output partitions, where the new index contained [0, 10), [10, 50), and [50, 100), respectively. See https://docs.dask.org/en/latest/dataframe-design.html#partitions. If not given (default), good divisions are calculated by immediately computing the data and looking at the distribution of its values. For large datasets, this can be expensive. Note that if ``sorted=True``, specified divisions are assumed to match the existing partitions in the data; if this is untrue you should leave divisions empty and call ``repartition`` after ``set_index``. inplace: bool, optional Modifying the DataFrame in place is not supported by Dask. Defaults to False. sort: bool, optional If ``True``, sort the DataFrame by the new index. Otherwise set the index on the individual existing partitions. Defaults to ``True``. shuffle_method: {'disk', 'tasks', 'p2p'}, optional Either ``'disk'`` for single-node operation or ``'tasks'`` and ``'p2p'`` for distributed operation. Will be inferred by your current scheduler. compute: bool, default False Whether or not to trigger an immediate computation. Defaults to False. Note, that even if you set ``compute=False``, an immediate computation will still be triggered if ``divisions`` is ``None``. partition_size: int, optional Desired size of each partitions in bytes. Only used when ``npartitions='auto'`` Examples -------- >>> import dask >>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1h").reset_index() >>> ddf2 = ddf.set_index("x") >>> ddf2 = ddf.set_index(ddf.x) >>> ddf2 = ddf.set_index(ddf.timestamp, sorted=True) A common case is when we have a datetime column that we know to be sorted and is cleanly divided by day. We can set this index for free by specifying both that the column is pre-sorted and the particular divisions along which is is separated >>> import pandas as pd >>> divisions = pd.date_range(start="2021-01-01", end="2021-01-07", freq='1D') >>> divisions DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05', '2021-01-06', '2021-01-07'], dtype='datetime64[ns]', freq='D') Note that ``len(divisions)`` is equal to ``npartitions + 1``. This is because ``divisions`` represents the upper and lower bounds of each partition. The first item is the lower bound of the first partition, the second item is the lower bound of the second partition and the upper bound of the first partition, and so on. The second-to-last item is the lower bound of the last partition, and the last (extra) item is the upper bound of the last partition. >>> ddf2 = ddf.set_index("timestamp", sorted=True, divisions=divisions.tolist()) If you'll be running `set_index` on the same (or similar) datasets repeatedly, you could save time by letting Dask calculate good divisions once, then copy-pasting them to reuse. This is especially helpful running in a Jupyter notebook: >>> ddf2 = ddf.set_index("name") # slow, calculates data distribution >>> ddf2.divisions # doctest: +SKIP ["Alice", "Laura", "Ursula", "Zelda"] >>> # ^ Now copy-paste this and edit the line above to: >>> # ddf2 = ddf.set_index("name", divisions=["Alice", "Laura", "Ursula", "Zelda"]) """ if inplace: raise NotImplementedError("The inplace= keyword is not supported") pre_sorted = sorted del sorted # Check other can be translated to column name or column object, possibly flattening it if not isinstance(other, str): # It may refer to several columns if isinstance(other, Sequence): # type: ignore[unreachable] # Accept ["a"], but not [["a"]] if len(other) == 1 and ( # type: ignore[unreachable] isinstance(other[0], str) or not isinstance(other[0], Sequence) ): other = other[0] else: raise NotImplementedError( "Dask dataframe does not yet support multi-indexes.\n" f"You tried to index with this index: {other}\n" "Indexes must be single columns only." ) # Or be a frame directly elif isinstance(other, DataFrame): # type: ignore[unreachable] raise NotImplementedError( "Dask dataframe does not yet support multi-indexes.\n" f"You tried to index with a frame with these columns: {list(other.columns)}\n" "Indexes must be single columns only." ) # If already a series if isinstance(other, Series): # If it's already the index, there's nothing to do if other._name == self.index._name: warnings.warn( "New index has same name as existing, this is a no-op.", UserWarning ) return self # If the name of a column/index else: # With the same name as the index, there's nothing to do either if other == self.index.name: warnings.warn( "New index has same name as existing, this is a no-op.", UserWarning ) return self # If a missing column, KeyError if other not in self.columns: raise KeyError( f"Data has no column '{other}': use any column of {list(self.columns)}" ) # Check divisions if divisions is not None: check_divisions(divisions) elif ( isinstance(other, Index) and other.known_divisions and other.npartitions == self.npartitions ): # If the index has the same number of partitions and known # divisions, then we can treat it as pre-sorted with known # divisions pre_sorted = True divisions = other.divisions # If index is already sorted, take advantage of that with set_sorted_index if pre_sorted: from dask.dataframe.shuffle import set_sorted_index return set_sorted_index( self, other, drop=drop, divisions=divisions, **kwargs ) else: from dask.dataframe.shuffle import set_index return set_index( self, other, drop=drop, npartitions=npartitions, divisions=divisions, sort=sort, **kwargs, ) @derived_from(pd.DataFrame) def pop(self, item): out = self[item] del self[item] return out @derived_from(pd.DataFrame) def nlargest(self, n=5, columns=no_default, split_every=None): if columns is no_default: raise TypeError( "DataFrame.nlargest() missing required positional argument: 'columns'" ) token = "dataframe-nlargest" return aca( self, chunk=M.nlargest, aggregate=M.nlargest, meta=self._meta, token=token, split_every=split_every, n=n, columns=columns, ) @derived_from(pd.DataFrame) def nsmallest(self, n=5, columns=no_default, split_every=None): if columns is no_default: raise TypeError( "DataFrame.nsmallest() missing required positional argument: 'columns'" ) token = "dataframe-nsmallest" return aca( self, chunk=M.nsmallest, aggregate=M.nsmallest, meta=self._meta, token=token, split_every=split_every, n=n, columns=columns, ) @derived_from(pd.DataFrame) def groupby( self, by=None, group_keys=GROUP_KEYS_DEFAULT, sort=None, observed=None, dropna=None, **kwargs, ): from dask.dataframe.groupby import DataFrameGroupBy return DataFrameGroupBy( self, by=by, group_keys=group_keys, sort=sort, observed=observed, dropna=dropna, **kwargs, ) @wraps(categorize) def categorize(self, columns=None, index=None, split_every=None, **kwargs): return categorize( self, columns=columns, index=index, split_every=split_every, **kwargs ) @derived_from(pd.DataFrame) def assign(self, **kwargs): data = self.copy() for k, v in kwargs.items(): if not ( isinstance(v, Scalar) or is_series_like(v) or callable(v) or pd.api.types.is_scalar(v) or is_index_like(v) or isinstance(v, Array) ): raise TypeError( f"Column assignment doesn't support type {typename(type(v))}" ) if callable(v): kwargs[k] = v(data) if isinstance(v, Array): from dask.dataframe.io import from_dask_array if len(v.shape) > 1: raise ValueError("Array assignment only supports 1-D arrays") if v.npartitions != data.npartitions: raise ValueError( "Number of partitions do not match " f"({v.npartitions} != {data.npartitions})" ) kwargs[k] = from_dask_array(v, index=data.index, meta=data._meta) pairs = [k, kwargs[k]] # Figure out columns of the output df2 = data._meta_nonempty.assign( **_extract_meta({k: kwargs[k]}, nonempty=True) ) data = elemwise(methods.assign, data, *pairs, meta=df2) return data @derived_from(pd.DataFrame, ua_args=["index"]) def rename(self, index=None, columns=None): if index is not None: raise ValueError("Cannot rename index.") # *args here is index, columns but columns arg is already used return self.map_partitions(M.rename, None, columns=columns) def query(self, expr, **kwargs): """Filter dataframe with complex expression Blocked version of pd.DataFrame.query Parameters ---------- expr: str The query string to evaluate. You can refer to column names that are not valid Python variable names by surrounding them in backticks. Dask does not fully support referring to variables using the '@' character, use f-strings or the ``local_dict`` keyword argument instead. Notes ----- This is like the sequential version except that this will also happen in many threads. This may conflict with ``numexpr`` which will use multiple threads itself. We recommend that you set ``numexpr`` to use a single thread: .. code-block:: python import numexpr numexpr.set_num_threads(1) See also -------- pandas.DataFrame.query pandas.eval Examples -------- >>> import pandas as pd >>> import dask.dataframe as dd >>> df = pd.DataFrame({'x': [1, 2, 1, 2], ... 'y': [1, 2, 3, 4], ... 'z z': [4, 3, 2, 1]}) >>> ddf = dd.from_pandas(df, npartitions=2) Refer to column names directly: >>> ddf.query('y > x').compute() x y z z 2 1 3 2 3 2 4 1 Refer to column name using backticks: >>> ddf.query('`z z` > x').compute() x y z z 0 1 1 4 1 2 2 3 2 1 3 2 Refer to variable name using f-strings: >>> value = 1 >>> ddf.query(f'x == {value}').compute() x y z z 0 1 1 4 2 1 3 2 Refer to variable name using ``local_dict``: >>> ddf.query('x == @value', local_dict={"value": value}).compute() x y z z 0 1 1 4 2 1 3 2 """ return self.map_partitions(M.query, expr, **kwargs) @derived_from(pd.DataFrame) def eval(self, expr, inplace=None, **kwargs): if inplace is None: inplace = False else: warnings.warn( "`inplace` is deprecated and will be removed in a futuere version.", FutureWarning, 2, ) if "=" in expr and inplace in (True, None): raise NotImplementedError( "Inplace eval not supported. Please use inplace=False" ) meta = self._meta.eval(expr, inplace=inplace, **kwargs) return self.map_partitions(M.eval, expr, meta=meta, inplace=inplace, **kwargs) @derived_from(pd.DataFrame) def dropna(self, how=no_default, subset=None, thresh=no_default): # These keywords are incompatible with each other. # Don't allow them both to be set. if how is not no_default and thresh is not no_default: raise TypeError( "You cannot set both the how and thresh arguments at the same time." ) # Only specify `how` or `thresh` keyword if specified by the user # so we utilize other `dropna` keyword defaults appropriately kwargs = {"subset": subset} if how is not no_default: kwargs["how"] = how elif thresh is not no_default: kwargs["thresh"] = thresh return self.map_partitions(M.dropna, **kwargs, enforce_metadata=False) @_dummy_numpy_dispatcher("out") @derived_from(pd.DataFrame) def clip(self, lower=None, upper=None, axis=None): return self.map_partitions( M.clip, lower=lower, upper=upper, enforce_metadata=False, # See: https://github.com/rapidsai/cudf/issues/14369 **({} if axis is None else {"axis": axis}), ) @derived_from(pd.DataFrame) def squeeze(self, axis=None): if axis in [None, 1]: if len(self.columns) == 1: return self[self.columns[0]] else: return self elif axis == 0: raise NotImplementedError( f"{type(self)} does not support squeeze along axis 0" ) elif axis not in [0, 1, None]: raise ValueError(f"No axis {axis} for object type {type(self)}") @derived_from(pd.DataFrame) def to_timestamp(self, freq=None, how="start", axis=0): df = elemwise(M.to_timestamp, self, freq, how, axis) df.divisions = tuple(pd.Index(self.divisions).to_timestamp(how=how, freq=freq)) return df @derived_from(pd.DataFrame) def explode(self, column): meta = self._meta.explode(column) return self.map_partitions(M.explode, column, meta=meta, enforce_metadata=False) def to_bag(self, index=False, format="tuple"): """Convert to a dask Bag of tuples of each row. Parameters ---------- index : bool, optional If True, the index is included as the first element of each tuple. Default is False. format : {"tuple", "dict", "frame"}, optional Whether to return a bag of tuples, dictionaries, or dataframe-like objects. Default is "tuple". If "frame", the original partitions of ``df`` will not be transformed in any way. """ from dask.dataframe.io import to_bag return to_bag(self, index, format) def to_parquet(self, path, *args, **kwargs): """See dd.to_parquet docstring for more information""" from dask.dataframe.io import to_parquet return to_parquet(self, path, *args, **kwargs) def to_orc(self, path, *args, **kwargs): """See dd.to_orc docstring for more information""" from dask.dataframe.io import to_orc return to_orc(self, path, *args, **kwargs) @derived_from(pd.DataFrame) def to_string(self, max_rows=5): # option_context doesn't affect return self._repr_data().to_string(max_rows=max_rows, show_dimensions=False) def _get_numeric_data(self, how="any", subset=None): # calculate columns to avoid unnecessary calculation numerics = self._meta._get_numeric_data() if len(numerics.columns) < len(self.columns): name = self._token_prefix + "-get_numeric_data" return self.map_partitions(M._get_numeric_data, meta=numerics, token=name) else: # use myself if all numerics return self @classmethod def _validate_axis(cls, axis=0, none_is_zero: bool = True) -> None | Literal[0, 1]: if axis not in (0, 1, "index", "columns", None): raise ValueError(f"No axis named {axis}") # convert to numeric axis numeric_axis: dict[str | None, Literal[0, 1]] = {"index": 0, "columns": 1} if none_is_zero: numeric_axis[None] = 0 return numeric_axis.get(axis, axis) @derived_from(pd.DataFrame) def drop(self, labels=None, axis=0, columns=None, errors="raise"): axis = self._validate_axis(axis) if axis == 0 and columns is not None: # Columns must be specified if axis==0 return self.map_partitions( drop_by_shallow_copy, columns, errors=errors, enforce_metadata=False ) elif axis == 1: return self.map_partitions( drop_by_shallow_copy, labels, errors=errors, enforce_metadata=False ) raise NotImplementedError( "Drop currently only works for axis=1 or when columns is not None" ) @_deprecated_kwarg("shuffle", "shuffle_method") def merge( self, right, how="inner", on=None, left_on=None, right_on=None, left_index=False, right_index=False, suffixes=("_x", "_y"), indicator=False, npartitions=None, shuffle_method=None, broadcast=None, ): """Merge the DataFrame with another DataFrame This will merge the two datasets, either on the indices, a certain column in each dataset or the index in one dataset and the column in another. Parameters ---------- right: dask.dataframe.DataFrame how : {'left', 'right', 'outer', 'inner'}, default: 'inner' How to handle the operation of the two objects: - left: use calling frame's index (or column if on is specified) - right: use other frame's index - outer: form union of calling frame's index (or column if on is specified) with other frame's index, and sort it lexicographically - inner: form intersection of calling frame's index (or column if on is specified) with other frame's index, preserving the order of the calling's one on : label or list Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames. left_on : label or list, or array-like Column to join on in the left DataFrame. Other than in pandas arrays and lists are only support if their length is 1. right_on : label or list, or array-like Column to join on in the right DataFrame. Other than in pandas arrays and lists are only support if their length is 1. left_index : boolean, default False Use the index from the left DataFrame as the join key. right_index : boolean, default False Use the index from the right DataFrame as the join key. suffixes : 2-length sequence (tuple, list, ...) Suffix to apply to overlapping column names in the left and right side, respectively indicator : boolean or string, default False If True, adds a column to output DataFrame called "_merge" with information on the source of each row. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column is Categorical-type and takes on a value of "left_only" for observations whose merge key only appears in `left` DataFrame, "right_only" for observations whose merge key only appears in `right` DataFrame, and "both" if the observation’s merge key is found in both. npartitions: int or None, optional The ideal number of output partitions. This is only utilised when performing a hash_join (merging on columns only). If ``None`` then ``npartitions = max(lhs.npartitions, rhs.npartitions)``. Default is ``None``. shuffle_method: {'disk', 'tasks', 'p2p'}, optional Either ``'disk'`` for single-node operation or ``'tasks'`` and ``'p2p'``` for distributed operation. Will be inferred by your current scheduler. broadcast: boolean or float, optional Whether to use a broadcast-based join in lieu of a shuffle-based join for supported cases. By default, a simple heuristic will be used to select the underlying algorithm. If a floating-point value is specified, that number will be used as the ``broadcast_bias`` within the simple heuristic (a large number makes Dask more likely to choose the ``broacast_join`` code path). See ``broadcast_join`` for more information. Notes ----- There are three ways to join dataframes: 1. Joining on indices. In this case the divisions are aligned using the function ``dask.dataframe.multi.align_partitions``. Afterwards, each partition is merged with the pandas merge function. 2. Joining one on index and one on column. In this case the divisions of dataframe merged by index (:math:`d_i`) are used to divide the column merged dataframe (:math:`d_c`) one using ``dask.dataframe.multi.rearrange_by_divisions``. In this case the merged dataframe (:math:`d_m`) has the exact same divisions as (:math:`d_i`). This can lead to issues if you merge multiple rows from (:math:`d_c`) to one row in (:math:`d_i`). 3. Joining both on columns. In this case a hash join is performed using ``dask.dataframe.multi.hash_join``. In some cases, you may see a ``MemoryError`` if the ``merge`` operation requires an internal ``shuffle``, because shuffling places all rows that have the same index in the same partition. To avoid this error, make sure all rows with the same ``on``-column value can fit on a single partition. """ if not is_dataframe_like(right): raise ValueError("right must be DataFrame") from dask.dataframe.multi import merge return merge( self, right, how=how, on=on, left_on=left_on, right_on=right_on, left_index=left_index, right_index=right_index, suffixes=suffixes, npartitions=npartitions, indicator=indicator, shuffle_method=shuffle_method, broadcast=broadcast, ) @_deprecated_kwarg("shuffle", "shuffle_method") @derived_from(pd.DataFrame) def join( self, other, on=None, how="left", lsuffix="", rsuffix="", npartitions=None, shuffle_method=None, ): if is_series_like(other) and hasattr(other, "name"): other = other.to_frame() if not is_dataframe_like(other): if not isinstance(other, list) or not all( [is_dataframe_like(o) for o in other] ): raise ValueError("other must be DataFrame or list of DataFrames") if how not in ["outer", "left"]: raise ValueError("merge_multi only supports left or outer joins") from dask.dataframe.multi import _recursive_pairwise_outer_join # If its an outer join we can use the full recursive pairwise join. if how == "outer": full = [self] + other return _recursive_pairwise_outer_join( full, on=on, lsuffix=lsuffix, rsuffix=rsuffix, npartitions=npartitions, shuffle_method=shuffle_method, ) else: # Do recursive pairwise join on everything _except_ the last join # where we need to do a left join. other = _recursive_pairwise_outer_join( other, on=on, lsuffix=lsuffix, rsuffix=rsuffix, npartitions=npartitions, shuffle_method=shuffle_method, ) from dask.dataframe.multi import merge return merge( self, other, how=how, left_index=on is None, right_index=True, left_on=on, suffixes=(lsuffix, rsuffix), npartitions=npartitions, shuffle_method=shuffle_method, ) if not PANDAS_GE_200: @derived_from(pd.DataFrame) def append(self, other, interleave_partitions=False): if isinstance(other, Series): msg = ( "Unable to appending dd.Series to dd.DataFrame." "Use pd.Series to append as row." ) raise ValueError(msg) elif is_series_like(other): other = other.to_frame().T return super().append(other, interleave_partitions=interleave_partitions) @derived_from(pd.DataFrame) def iterrows(self): for i in range(self.npartitions): df = self.get_partition(i).compute() yield from df.iterrows() @derived_from(pd.DataFrame) def itertuples(self, index=True, name="Pandas"): for i in range(self.npartitions): df = self.get_partition(i).compute() yield from df.itertuples(index=index, name=name) @derived_from(pd.DataFrame) def items(self): for col_idx, label in enumerate(self.columns): yield label, self.iloc[:, col_idx] @classmethod def _bind_operator_method(cls, name, op, original=pd.DataFrame): """bind operator method like DataFrame.add to this class""" # name must be explicitly passed for div method whose name is truediv def meth(self, other, axis="columns", level=None, fill_value=None): if level is not None: raise NotImplementedError("level must be None") axis = self._validate_axis(axis) if axis in (1, "columns"): # When axis=1 and other is a series, `other` is transposed # and the operator is applied broadcast across rows. This # isn't supported with dd.Series. if isinstance(other, Series): msg = f"Unable to {name} dd.Series with axis=1" raise ValueError(msg) elif is_series_like(other): # Special case for pd.Series to avoid unwanted partitioning # of other. We pass it in as a kwarg to prevent this. meta = _emulate( op, self, other=other, axis=axis, fill_value=fill_value ) return map_partitions( op, self, other=other, meta=meta, axis=axis, fill_value=fill_value, enforce_metadata=False, ) meta = _emulate(op, self, other, axis=axis, fill_value=fill_value) return map_partitions( op, self, other, meta=meta, axis=axis, fill_value=fill_value, enforce_metadata=False, ) meth.__name__ = name setattr(cls, name, derived_from(original)(meth)) @classmethod def _bind_comparison_method(cls, name, comparison, original=pd.DataFrame): """bind comparison method like DataFrame.eq to this class""" def meth(self, other, axis="columns", level=None): if level is not None: raise NotImplementedError("level must be None") axis = self._validate_axis(axis) return elemwise(comparison, self, other, axis=axis) meth.__name__ = name setattr(cls, name, derived_from(original)(meth)) @insert_meta_param_description(pad=12) def apply( self, func, axis=0, broadcast=None, raw=False, reduce=None, args=(), meta=no_default, result_type=None, **kwds, ): """Parallel version of pandas.DataFrame.apply This mimics the pandas version except for the following: 1. Only ``axis=1`` is supported (and must be specified explicitly). 2. The user should provide output metadata via the `meta` keyword. Parameters ---------- func : function Function to apply to each column/row axis : {0 or 'index', 1 or 'columns'}, default 0 - 0 or 'index': apply function to each column (NOT SUPPORTED) - 1 or 'columns': apply function to each row $META args : tuple Positional arguments to pass to function in addition to the array/series Additional keyword arguments will be passed as keywords to the function Returns ------- applied : Series or DataFrame Examples -------- >>> import pandas as pd >>> import dask.dataframe as dd >>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5], ... 'y': [1., 2., 3., 4., 5.]}) >>> ddf = dd.from_pandas(df, npartitions=2) Apply a function to row-wise passing in extra arguments in ``args`` and ``kwargs``: >>> def myadd(row, a, b=1): ... return row.sum() + a + b >>> res = ddf.apply(myadd, axis=1, args=(2,), b=1.5) # doctest: +SKIP By default, dask tries to infer the output metadata by running your provided function on some fake data. This works well in many cases, but can sometimes be expensive, or even fail. To avoid this, you can manually specify the output metadata with the ``meta`` keyword. This can be specified in many forms, for more information see ``dask.dataframe.utils.make_meta``. Here we specify the output is a Series with name ``'x'``, and dtype ``float64``: >>> res = ddf.apply(myadd, axis=1, args=(2,), b=1.5, meta=('x', 'f8')) In the case where the metadata doesn't change, you can also pass in the object itself directly: >>> res = ddf.apply(lambda row: row + 1, axis=1, meta=ddf) See Also -------- dask.DataFrame.map_partitions """ if broadcast is not None: warnings.warn( "The `broadcast` argument is no longer used/supported. " "It will be dropped in a future release.", category=FutureWarning, ) axis = self._validate_axis(axis) pandas_kwargs = {"axis": axis, "raw": raw, "result_type": result_type} kwds.update(pandas_kwargs) if axis == 0: msg = ( "dd.DataFrame.apply only supports axis=1\n" " Try: df.apply(func, axis=1)" ) raise NotImplementedError(msg) if meta is no_default: meta = _emulate( M.apply, self._meta_nonempty, func, args=args, udf=True, **kwds ) warnings.warn(meta_warning(meta)) kwds.update({"parent_meta": self._meta}) return map_partitions(M.apply, self, func, args=args, meta=meta, **kwds) @derived_from(pd.DataFrame) def applymap(self, func, meta=no_default): # Let pandas raise deprecation warnings self._meta.applymap(func) return elemwise(methods.applymap, self, func, meta=meta) def map(self, func, meta=no_default, na_action=None): if not PANDAS_GE_210: raise NotImplementedError( f"DataFrame.map requires pandas>=2.1.0, but pandas={PANDAS_VERSION} is " "installed." ) return elemwise(M.map, self, func, meta=meta, na_action=na_action) @derived_from(pd.DataFrame) def round(self, decimals=0): return elemwise(M.round, self, decimals) @derived_from(pd.DataFrame) def nunique(self, split_every=False, dropna=True, axis=0): if axis == 1: # split_every not used for axis=1 meta = self._meta_nonempty.nunique(axis=axis) return self.map_partitions( M.nunique, meta=meta, token="series-nunique", axis=axis, dropna=dropna, enforce_metadata=False, ) else: nunique_list = [ self.iloc[:, i].nunique(split_every=split_every, dropna=dropna) for i in range(len(self.columns)) ] name = "series-" + tokenize(*nunique_list) dsk = { (name, 0): ( apply, pd.Series, [[(s._name, 0) for s in nunique_list]], {"index": self.columns}, ) } graph = HighLevelGraph.from_collections( name, dsk, dependencies=nunique_list ) return Series(graph, name, self._meta.nunique(), (None, None)) @derived_from(pd.DataFrame) def mode(self, dropna=True, split_every=False, numeric_only=False): mode_series_list = [] for col_index in range(len(self.columns)): col_series = self.iloc[:, col_index] if numeric_only and not pd.api.types.is_numeric_dtype(col_series.dtype): continue mode_series = Series.mode( col_series, dropna=dropna, split_every=split_every ) mode_series_list.append(mode_series) name = "concat-" + tokenize(*mode_series_list) dsk = { (name, 0): ( apply, methods.concat, [[(df._name, 0) for df in mode_series_list]], {"axis": 1}, ) } meta = methods.concat([df._meta for df in mode_series_list], axis=1) graph = HighLevelGraph.from_collections( name, dsk, dependencies=mode_series_list ) ddf = new_dd_object(graph, name, meta, divisions=(None, None)) return ddf @derived_from(pd.DataFrame) def cov(self, min_periods=None, numeric_only=no_default, split_every=False): return _cov_corr( self, min_periods, numeric_only=numeric_only, split_every=split_every ) @derived_from(pd.DataFrame) def corr( self, method="pearson", min_periods=None, numeric_only=no_default, split_every=False, ): if method != "pearson": raise NotImplementedError("Only Pearson correlation has been implemented") return _cov_corr( self, min_periods, True, numeric_only=numeric_only, split_every=split_every ) def info(self, buf=None, verbose=False, memory_usage=False): """ Concise summary of a Dask DataFrame. """ if buf is None: import sys buf = sys.stdout lines = [str(type(self))] if len(self.columns) == 0: lines.append(f"{type(self.index._meta).__name__}: 0 entries") lines.append(f"Empty {type(self).__name__}") if PANDAS_GE_150: # pandas dataframe started adding a newline when info is called. lines.append("") put_lines(buf, lines) return # Group and execute the required computations computations = {} if verbose: memory_usage = True computations.update({"index": self.index, "count": self.count()}) if memory_usage: computations.update( {"memory_usage": self.map_partitions(M.memory_usage, index=True)} ) computations = dict( zip(computations.keys(), da.compute(*computations.values())) ) if verbose: import textwrap index = computations["index"] counts = computations["count"] lines.append(index_summary(index)) lines.append(f"Data columns (total {len(self.columns)} columns):") from pandas.io.formats.printing import pprint_thing space = max(len(pprint_thing(k)) for k in self.columns) + 1 column_width = max(space, 7) header = ( textwrap.dedent( """\ # {{column:<{column_width}}} Non-Null Count Dtype --- {{underl:<{column_width}}} -------------- -----""" ) .format(column_width=column_width) .format(column="Column", underl="------") ) column_template = textwrap.dedent( """\ {{i:^3}} {{name:<{column_width}}} {{count}} non-null {{dtype}}""".format( column_width=column_width ) ) column_info = [ column_template.format( i=pprint_thing(i), name=pprint_thing(name), count=pprint_thing(count), dtype=pprint_thing(dtype), ) for i, (name, count, dtype) in enumerate( zip(self.columns, counts, self.dtypes) ) ] lines.extend(header.split("\n")) else: column_info = [index_summary(self.columns, name="Columns")] lines.extend(column_info) dtype_counts = [ "%s(%d)" % k for k in sorted(self.dtypes.value_counts().items(), key=str) ] lines.append("dtypes: {}".format(", ".join(dtype_counts))) if memory_usage: memory_int = computations["memory_usage"].sum() lines.append(f"memory usage: {memory_repr(memory_int)}\n") put_lines(buf, lines) @derived_from(pd.DataFrame) def memory_usage(self, index=True, deep=False): result = self.map_partitions(M.memory_usage, index=index, deep=deep) result = result.groupby(result.index).sum() return result def pivot_table(self, index=None, columns=None, values=None, aggfunc="mean"): """ Create a spreadsheet-style pivot table as a DataFrame. Target ``columns`` must have category dtype to infer result's ``columns``. ``index``, ``columns``, ``values`` and ``aggfunc`` must be all scalar. Parameters ---------- values : scalar column to aggregate index : scalar column to be index columns : scalar column to be columns aggfunc : {'mean', 'sum', 'count'}, default 'mean' Returns ------- table : DataFrame """ from dask.dataframe.reshape import pivot_table return pivot_table( self, index=index, columns=columns, values=values, aggfunc=aggfunc ) def melt( self, id_vars=None, value_vars=None, var_name=None, value_name="value", col_level=None, ): """ Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (``id_vars``), while all other columns, considered measured variables (``value_vars``), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'. Parameters ---------- frame : DataFrame id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables. value_vars : tuple, list, or ndarray, optional Column(s) to unpivot. If not specified, uses all columns that are not set as `id_vars`. var_name : scalar Name to use for the 'variable' column. If None it uses ``frame.columns.name`` or 'variable'. value_name : scalar, default 'value' Name to use for the 'value' column. col_level : int or string, optional If columns are a MultiIndex then use this level to melt. Returns ------- DataFrame Unpivoted DataFrame. See Also -------- pandas.DataFrame.melt """ from dask.dataframe.reshape import melt return melt( self, id_vars=id_vars, value_vars=value_vars, var_name=var_name, value_name=value_name, col_level=col_level, ) def to_records(self, index=False, lengths=None): from dask.dataframe.io import to_records if lengths is True: lengths = tuple(self.map_partitions(len).compute()) records = to_records(self) chunks = self._validate_chunks(records, lengths) records._chunks = (chunks[0],) return records @derived_from(pd.DataFrame) def to_html(self, max_rows=5): # pd.Series doesn't have html repr data = self._repr_data().to_html(max_rows=max_rows, show_dimensions=False) return get_template("dataframe.html.j2").render( data=data, name=self._name, layers=maybe_pluralize(len(self.dask.layers), "graph layer"), ) def _repr_data(self): meta = self._meta index = self._repr_divisions cols = meta.columns if len(cols) == 0: series_df = pd.DataFrame([[]] * len(index), columns=cols, index=index) else: series_df = pd.concat( [_repr_data_series(s, index=index) for _, s in meta.items()], axis=1 ) return series_df def _repr_html_(self): data = self._repr_data().to_html( max_rows=5, show_dimensions=False, notebook=True ) return get_template("dataframe.html.j2").render( data=data, name=self._name, layers=maybe_pluralize(len(self.dask.layers), "graph layer"), ) def _select_columns_or_index(self, columns_or_index): """ Parameters ---------- columns_or_index Column or index name, or a list of these Returns ------- dd.DataFrame Dask DataFrame with columns corresponding to each column or index level in columns_or_index. If included, the column corresponding to the index level is named _index """ # Ensure columns_or_index is a list columns_or_index = ( columns_or_index if isinstance(columns_or_index, list) else [columns_or_index] ) column_names = [ n for n in columns_or_index if self._is_column_label_reference(n) ] selected_df = self[column_names] if self._contains_index_name(columns_or_index): # Index name was included selected_df = selected_df.assign(_index=self.index) return selected_df def _is_column_label_reference(self, key): """ Test whether a key is a column label reference To be considered a column label reference, `key` must match the name of at least one column. """ return ( not is_dask_collection(key) and (np.isscalar(key) or isinstance(key, tuple)) and key in self.columns ) @classmethod def from_dict( cls, data, *, npartitions, orient="columns", dtype=None, columns=None ): """ Construct a Dask DataFrame from a Python Dictionary See Also -------- dask.dataframe.from_dict """ from dask.dataframe.io import from_dict return from_dict( data, npartitions, orient=orient, dtype=dtype, columns=columns, constructor=cls._partition_type, ) # bind operators # TODO: dynamically bound operators are defeating type annotations for op in [ operator.abs, operator.add, operator.and_, operator.eq, operator.gt, operator.ge, operator.inv, operator.lt, operator.le, operator.mod, operator.mul, operator.ne, operator.neg, operator.or_, operator.pow, operator.sub, operator.truediv, operator.floordiv, operator.xor, ]: _Frame._bind_operator(op) Scalar._bind_operator(op) for name in [ "add", "sub", "mul", "div", "divide", "truediv", "floordiv", "mod", "pow", "radd", "rsub", "rmul", "rdiv", "rtruediv", "rfloordiv", "rmod", "rpow", ]: meth = getattr(pd.DataFrame, name) DataFrame._bind_operator_method(name, meth) meth = getattr(pd.Series, name) Series._bind_operator_method(name, meth) for name in ["lt", "gt", "le", "ge", "ne", "eq"]: meth = getattr(pd.DataFrame, name) DataFrame._bind_comparison_method(name, meth) meth = getattr(pd.Series, name) Series._bind_comparison_method(name, meth) def is_broadcastable(dfs, s): """ This Series is broadcastable against another dataframe in the sequence """ def compare(s, df): try: return s.divisions == (df.columns.min(), df.columns.max()) except TypeError: return False return ( isinstance(s, Series) and s.npartitions == 1 and s.known_divisions and any(compare(s, df) for df in dfs if isinstance(df, DataFrame)) ) def elemwise(op, *args, meta=no_default, out=None, transform_divisions=True, **kwargs): """Elementwise operation for Dask dataframes Parameters ---------- op: callable Function to apply across input dataframes *args: DataFrames, Series, Scalars, Arrays, etc. The arguments of the operation meta: pd.DataFrame, pd.Series (optional) Valid metadata for the operation. Will evaluate on a small piece of data if not provided. transform_divisions: boolean If the input is a ``dask.dataframe.Index`` we normally will also apply the function onto the divisions and apply those transformed divisions to the output. You can pass ``transform_divisions=False`` to override this behavior out : dask.DataFrame, dask.Series, dask.Scalar, or None If out is a dask.DataFrame, dask.Series or dask.Scalar then this overwrites the contents of it with the result **kwargs: scalars Examples -------- >>> elemwise(operator.add, df.x, df.y) # doctest: +SKIP """ _name = funcname(op) + "-" + tokenize(op, *args, **kwargs) args = _maybe_from_pandas(args) from dask.dataframe.multi import _maybe_align_partitions args = _maybe_align_partitions(args) dasks = [arg for arg in args if isinstance(arg, (_Frame, Scalar, Array))] dfs = [df for df in dasks if isinstance(df, _Frame)] # Clean up dask arrays if present deps = dasks.copy() for i, a in enumerate(dasks): if not isinstance(a, Array): continue # Ensure that they have similar-ish chunk structure if not all(not a.chunks or len(a.chunks[0]) == df.npartitions for df in dfs): msg = ( "When combining dask arrays with dataframes they must " "match chunking exactly. Operation: %s" % funcname(op) ) raise ValueError(msg) # Rechunk to have a single chunk along all other axes if a.ndim > 1: a = a.rechunk({i + 1: d for i, d in enumerate(a.shape[1:])}) dasks[i] = a divisions = dfs[0].divisions if transform_divisions and isinstance(dfs[0], Index) and len(dfs) == 1: try: divisions = op( *[pd.Index(arg.divisions) if arg is dfs[0] else arg for arg in args], **kwargs, ) if isinstance(divisions, pd.Index): divisions = methods.tolist(divisions) except Exception: pass else: if not valid_divisions(divisions): divisions = [None] * (dfs[0].npartitions + 1) _is_broadcastable = partial(is_broadcastable, dfs) dfs = list(remove(_is_broadcastable, dfs)) other = [ (i, arg) for i, arg in enumerate(args) if not isinstance(arg, (_Frame, Scalar, Array)) ] # adjust the key length of Scalar dsk = partitionwise_graph(op, _name, *args, **kwargs) graph = HighLevelGraph.from_collections(_name, dsk, dependencies=deps) if meta is no_default: if len(dfs) >= 2 and not all(hasattr(d, "npartitions") for d in dasks): # should not occur in current funcs msg = "elemwise with 2 or more DataFrames and Scalar is not supported" raise NotImplementedError(msg) # For broadcastable series, use no rows. parts = [ d._meta if _is_broadcastable(d) else np.empty((), dtype=d.dtype) if isinstance(d, Array) else d._meta_nonempty for d in dasks ] with raise_on_meta_error(funcname(op)): meta = partial_by_order(*parts, function=op, other=other) result = new_dd_object(graph, _name, meta, divisions) return handle_out(out, result) def handle_out(out, result): """Handle out parameters If out is a dask.DataFrame, dask.Series or dask.Scalar then this overwrites the contents of it with the result """ if isinstance(out, tuple): if len(out) == 1: out = out[0] elif len(out) > 1: raise NotImplementedError("The out parameter is not fully supported") else: out = None # Notice, we use .__class__ as opposed to type() in order to support # object proxies see if out is not None and out.__class__ != result.__class__: raise TypeError( "Mismatched types between result and out parameter. " "out=%s, result=%s" % (str(type(out)), str(type(result))) ) if isinstance(out, DataFrame): if len(out.columns) != len(result.columns): raise ValueError( "Mismatched columns count between result and out parameter. " "out=%s, result=%s" % (str(len(out.columns)), str(len(result.columns))) ) if isinstance(out, (Series, DataFrame, Scalar)): out._meta = result._meta out._name = result._name out.dask = result.dask if not isinstance(out, Scalar): out._divisions = result.divisions return result elif out is not None: msg = ( "The out parameter is not fully supported." " Received type %s, expected %s " % ( typename(type(out)), typename(type(result)), ) ) raise NotImplementedError(msg) else: return result def _maybe_from_pandas(dfs): from dask.dataframe.io import from_pandas dfs = [ from_pandas(df, 1) if (is_series_like(df) or is_dataframe_like(df)) and not is_dask_collection(df) else df for df in dfs ] return dfs def hash_shard( df, nparts, split_out_setup=None, split_out_setup_kwargs=None, ignore_index=False ): if split_out_setup: h = split_out_setup(df, **(split_out_setup_kwargs or {})) else: h = df h = hash_object_dispatch(h, index=False) return group_split_dispatch(df, h % nparts, nparts, ignore_index=ignore_index) def split_evenly(df, k): """Split dataframe into k roughly equal parts""" divisions = np.linspace(0, len(df), k + 1).astype(int) return {i: df.iloc[divisions[i] : divisions[i + 1]] for i in range(k)} def split_out_on_index(df): h = df.index if isinstance(h, pd.MultiIndex): h = meta_frame_constructor(df)([], index=h).reset_index() return h def split_out_on_cols(df, cols=None): return df[cols] @insert_meta_param_description def apply_concat_apply( args, chunk=None, aggregate=None, combine=None, meta=no_default, token=None, chunk_kwargs=None, aggregate_kwargs=None, combine_kwargs=None, split_every=None, split_out=None, split_out_setup=None, split_out_setup_kwargs=None, sort=None, ignore_index=False, **kwargs, ): """Apply a function to blocks, then concat, then apply again 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 combine : function concatenated-block -> block, optional Function to operate on intermediate concatenated results of chunk in a tree-reduction. If not provided, defaults to aggregate. $META 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. combine_kwargs : dict, optional Keywords for the combine function only. split_every : int, optional Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used, and all intermediates will be concatenated and passed to ``aggregate``. Default is 8. split_out : int, optional Number of output partitions. Split occurs after first chunk reduction. split_out_setup : callable, optional If provided, this function is called on each chunk before performing the hash-split. It should return a pandas object, where each row (excluding the index) is hashed. If not provided, the chunk is hashed as is. split_out_setup_kwargs : dict, optional Keywords for the `split_out_setup` function only. sort : bool, default None If allowed, sort the keys of the output aggregation. ignore_index : bool, default False If True, do not preserve index values throughout ACA operations. kwargs : All remaining keywords will be passed to ``chunk``, ``aggregate``, and ``combine``. Examples -------- >>> def chunk(a_block, b_block): ... pass >>> def agg(df): ... pass >>> apply_concat_apply([a, b], chunk=chunk, aggregate=agg) # doctest: +SKIP """ if split_out is None: split_out = 1 if chunk_kwargs is None: chunk_kwargs = dict() if aggregate_kwargs is None: aggregate_kwargs = dict() chunk_kwargs.update(kwargs) aggregate_kwargs.update(kwargs) if combine is None: if combine_kwargs: raise ValueError("`combine_kwargs` provided with no `combine`") combine = aggregate combine_kwargs = aggregate_kwargs else: if combine_kwargs is None: combine_kwargs = dict() combine_kwargs.update(kwargs) 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 < 2 or not isinstance(split_every, Integral): raise ValueError("split_every must be an integer >= 2") token_key = tokenize( token or (chunk, aggregate), meta, args, chunk_kwargs, aggregate_kwargs, combine_kwargs, split_every, split_out, split_out_setup, split_out_setup_kwargs, ) # Blockwise Chunk Layer chunk_name = f"{token or funcname(chunk)}-chunk-{token_key}" chunked = map_bag_partitions( chunk, # Convert _Frame collections to Bag *[ arg.to_bag(format="frame") if isinstance(arg, _Frame) else arg for arg in args ], token=chunk_name, **chunk_kwargs, ) # NOTE: `chunked` is now a Bag collection. # We don't use a DataFrame collection, because # the partitions may not contain dataframe-like # objects anymore. # Blockwise Split Layer if split_out and split_out > 1: chunked = chunked.map_partitions( hash_shard, split_out, split_out_setup, split_out_setup_kwargs, ignore_index, token="split-%s" % token_key, ) # Handle sort behavior if sort is not None: if sort and split_out > 1: raise NotImplementedError( "Cannot guarantee sorted keys for `split_out>1`." " Try using split_out=1, or grouping with sort=False." ) aggregate_kwargs = aggregate_kwargs or {} aggregate_kwargs["sort"] = sort # Tree-Reduction Layer final_name = f"{token or funcname(aggregate)}-agg-{token_key}" layer = DataFrameTreeReduction( final_name, chunked.name, npartitions, partial(_concat, ignore_index=ignore_index), partial(combine, **combine_kwargs) if combine_kwargs else combine, finalize_func=partial(aggregate, **aggregate_kwargs) if aggregate_kwargs else aggregate, split_every=split_every, split_out=split_out if (split_out and split_out > 1) else None, tree_node_name=f"{token or funcname(combine)}-combine-{token_key}", ) if meta is no_default: meta_chunk = _emulate(chunk, *args, udf=True, **chunk_kwargs) meta = _emulate( aggregate, _concat([meta_chunk], ignore_index), udf=True, **aggregate_kwargs ) meta = make_meta( meta, index=(getattr(make_meta(dfs[0]), "index", None) if dfs else None), parent_meta=dfs[0]._meta, ) graph = HighLevelGraph.from_collections(final_name, layer, dependencies=(chunked,)) divisions = [None] * ((split_out or 1) + 1) return new_dd_object(graph, final_name, meta, divisions, parent_meta=dfs[0]._meta) aca = apply_concat_apply def _extract_meta(x, nonempty=False): """ Extract internal cache data (``_meta``) from dd.DataFrame / dd.Series """ if isinstance(x, (Scalar, _Frame)): return x._meta_nonempty if nonempty else x._meta elif isinstance(x, list): return [_extract_meta(_x, nonempty) for _x in x] elif isinstance(x, tuple): return tuple(_extract_meta(_x, nonempty) for _x in x) elif isinstance(x, dict): res = {} for k in x: res[k] = _extract_meta(x[k], nonempty) return res elif isinstance(x, Delayed): raise ValueError( "Cannot infer dataframe metadata with a `dask.delayed` argument" ) else: return x def _emulate(func, *args, udf=False, **kwargs): """ Apply a function using args / kwargs. If arguments contain dd.DataFrame / dd.Series, using internal cache (``_meta``) for calculation """ with raise_on_meta_error(funcname(func), udf=udf), check_numeric_only_deprecation(): return func(*_extract_meta(args, True), **_extract_meta(kwargs, True)) @insert_meta_param_description def map_partitions( func, *args, meta=no_default, enforce_metadata=True, transform_divisions=True, align_dataframes=True, **kwargs, ): """Apply Python function on each DataFrame partition. Parameters ---------- func : function Function applied to each partition. args, kwargs : Arguments and keywords to pass to the function. At least one of the args should be a Dask.dataframe. Arguments and keywords may contain ``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). 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. 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 """ name = kwargs.pop("token", None) parent_meta = kwargs.pop("parent_meta", None) assert callable(func) if name is not None: token = tokenize(meta, *args, **kwargs) else: name = funcname(func) token = tokenize(func, meta, *args, **kwargs) name = f"{name}-{token}" from dask.dataframe.multi import _maybe_align_partitions 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_partitions` 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 = [] for arg in args: if isinstance(arg, _Frame): 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 divisions = _get_divisions_map_partitions( align_dataframes, transform_divisions, dfs, func, args, kwargs ) 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, *args2, dependencies=dependencies, _func=func, _meta=meta, **kwargs3, ) else: kwargs4 = kwargs if simple else kwargs3 dsk = partitionwise_graph( func, name, *args2, **kwargs4, dependencies=dependencies ) graph = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies) return new_dd_object(graph, name, meta, divisions) def _get_divisions_map_partitions( align_dataframes, transform_divisions, dfs, func, args, kwargs ): """ Helper to get divisions for map_partitions and map_overlap output. """ if align_dataframes: divisions = dfs[0].divisions else: # Unaligned, dfs is a mix of 1 partition and 1+ partition dataframes, # use longest divisions found divisions = max((d.divisions for d in dfs), key=len) if transform_divisions and isinstance(dfs[0], Index) and len(dfs) == 1: try: divisions = func( *[pd.Index(a.divisions) if a is dfs[0] else a for a in args], **kwargs ) if isinstance(divisions, pd.Index): divisions = methods.tolist(divisions) except Exception: pass else: if not valid_divisions(divisions): divisions = [None] * (dfs[0].npartitions + 1) return divisions def _get_meta_map_partitions(args, dfs, func, kwargs, meta, parent_meta): """ Helper to generate metadata for map_partitions and map_overlap output. """ meta_index = getattr(make_meta(dfs[0]), "index", None) if dfs else None if parent_meta is None and dfs: parent_meta = dfs[0]._meta if meta is no_default: # Use non-normalized kwargs here, as we want the real values (not # delayed values) meta = _emulate(func, *args, udf=True, **kwargs) meta_is_emulated = True else: meta = make_meta(meta, index=meta_index, parent_meta=parent_meta) meta_is_emulated = False if not (has_parallel_type(meta) or is_arraylike(meta) and meta.shape) and not all( isinstance(arg, Scalar) for arg in args ): if not meta_is_emulated: warnings.warn( "Meta is not valid, `map_partitions` and `map_overlap` expects output to be a pandas object. " "Try passing a pandas object as meta or a dict or tuple representing the " "(name, dtype) of the columns. In the future the meta you passed will not work.", FutureWarning, ) # If `meta` is not a pandas object, the concatenated results will be a # different type meta = make_meta(_concat([meta]), index=meta_index) # Ensure meta is empty series meta = make_meta(meta, parent_meta=parent_meta) return meta def apply_and_enforce(*args, **kwargs): """Apply a function, and enforce the output to match meta Ensures the output has the same columns, even if empty.""" func = kwargs.pop("_func") meta = kwargs.pop("_meta") df = func(*args, **kwargs) if is_dataframe_like(df) or is_series_like(df) or is_index_like(df): if not len(df): return meta if is_dataframe_like(df): check_matching_columns(meta, df) c = meta.columns else: c = meta.name return _rename(c, df) return df def _rename(columns, df): """ Rename columns of pd.DataFrame or name of pd.Series. Not for dd.DataFrame or dd.Series. Parameters ---------- columns : tuple, string, pd.DataFrame or pd.Series Column names, Series name or pandas instance which has the target column names / name. df : pd.DataFrame or pd.Series target DataFrame / Series to be renamed """ assert not isinstance(df, _Frame) if columns is no_default: return df if isinstance(columns, Iterator): columns = list(columns) if is_dataframe_like(df): if is_dataframe_like(columns): columns = columns.columns if not isinstance(columns, pd.Index): columns = pd.Index(columns) if ( len(columns) == len(df.columns) and type(columns) is type(df.columns) and columns.dtype == df.columns.dtype and columns.equals(df.columns) ): # if target is identical, rename is not necessary return df # deep=False doesn't doesn't copy any data/indices, so this is cheap df = df.copy(deep=False) df.columns = columns return df elif is_series_like(df) or is_index_like(df): if is_series_like(columns) or is_index_like(columns): columns = columns.name if df.name == columns: return df return df.rename(columns) # map_partition may pass other types return df def _rename_dask(df, names): """ Destructively rename columns of dd.DataFrame or name of dd.Series. Not for pd.DataFrame or pd.Series. Internally used to overwrite dd.DataFrame.columns and dd.Series.name We can't use map_partition because it applies function then rename Parameters ---------- df : dd.DataFrame or dd.Series target DataFrame / Series to be renamed names : tuple, string Column names/Series name """ assert isinstance(df, _Frame) metadata = _rename(names, df._meta) name = f"rename-{tokenize(df, metadata)}" dsk = partitionwise_graph(_rename, name, metadata, df) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[df]) return new_dd_object(graph, name, metadata, df.divisions) def quantile(df, q, method="default"): """Approximate quantiles of Series. Parameters ---------- q : list/array of floats Iterable of numbers ranging from 0 to 100 for the desired quantiles method : {'default', 'tdigest', 'dask'}, optional What method to use. By default will use dask's internal custom algorithm (``'dask'``). If set to ``'tdigest'`` will use tdigest for floats and ints and fallback to the ``'dask'`` otherwise. """ # current implementation needs q to be sorted so # sort if array-like, otherwise leave it alone q_ndarray = np.array(q) if q_ndarray.ndim > 0: q_ndarray.sort(kind="mergesort") q = q_ndarray assert isinstance(df, Series) allowed_methods = ["default", "dask", "tdigest"] if method not in allowed_methods: raise ValueError("method can only be 'default', 'dask' or 'tdigest'") if method == "default": internal_method = "dask" else: internal_method = method # currently, only Series has quantile method if isinstance(df, Index): series_typ = df._meta.to_series()._constructor meta = df._meta_nonempty.to_series().quantile(q) else: if is_series_like(df._meta): series_typ = df._meta._constructor else: series_typ = df._meta._constructor_sliced meta = df._meta_nonempty.quantile(q) if is_series_like(meta): # Index.quantile(list-like) must be pd.Series, not pd.Index df_name = df.name finalize_tsk = lambda tsk: (series_typ, tsk, q, None, df_name) return_type = Series else: # repack as Series object to convert scalars from NumPy back before extracting finalize_tsk = lambda tsk: ( lambda *args, **kwargs: series_typ(*args, **kwargs)[0], tsk, [0], ) return_type = Scalar q = [q] # pandas uses quantile in [0, 1] # numpy / everyone else uses [0, 100] qs = np.asarray(q) * 100 token = tokenize(df, qs) if len(qs) == 0: name = "quantiles-" + token empty_index = pd.Index([], dtype=float) return Series( {(name, 0): series_typ([], name=df.name, index=empty_index, dtype="float")}, name, df._meta, [None, None], ) else: new_divisions = [np.min(q), np.max(q)] df = df.dropna() if internal_method == "tdigest" and ( np.issubdtype(df.dtype, np.floating) or np.issubdtype(df.dtype, np.integer) ): from dask.utils import import_required import_required( "crick", "crick is a required dependency for using the t-digest method." ) from dask.array.percentile import _percentiles_from_tdigest, _tdigest_chunk name = "quantiles_tdigest-1-" + token val_dsk = { (name, i): (_tdigest_chunk, (getattr, key, "values")) for i, key in enumerate(df.__dask_keys__()) } name2 = "quantiles_tdigest-2-" + token merge_dsk = { (name2, 0): finalize_tsk((_percentiles_from_tdigest, qs, sorted(val_dsk))) } else: from dask.array.dispatch import percentile_lookup as _percentile from dask.array.percentile import merge_percentiles # Add 0 and 100 during calculation for more robust behavior (hopefully) calc_qs = np.pad(qs, 1, mode="constant") calc_qs[-1] = 100 name = "quantiles-1-" + token val_dsk = { (name, i): (_percentile, key, calc_qs) for i, key in enumerate(df.__dask_keys__()) } name2 = "quantiles-2-" + token merge_dsk = { (name2, 0): finalize_tsk( ( merge_percentiles, qs, [calc_qs] * df.npartitions, sorted(val_dsk), "lower", None, False, ) ) } dsk = merge(val_dsk, merge_dsk) graph = HighLevelGraph.from_collections(name2, dsk, dependencies=[df]) return return_type(graph, name2, meta, new_divisions) def _cov_corr( df, min_periods=None, corr=False, scalar=False, numeric_only=no_default, split_every=False, ): """DataFrame covariance and pearson correlation. Computes pairwise covariance or correlation of columns, excluding NA/null values. Parameters ---------- df : DataFrame min_periods : int, optional Minimum number of observations required per pair of columns to have a valid result. corr : bool, optional If True, compute the Pearson correlation. If False [default], compute the covariance. scalar : bool, optional If True, compute covariance between two variables as a scalar. Only valid if `df` has 2 columns. If False [default], compute the entire covariance/correlation matrix. split_every : int, optional Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used. Default is False. """ if min_periods is None: min_periods = 2 elif min_periods < 2: raise ValueError("min_periods must be >= 2") if split_every is False: split_every = df.npartitions elif split_every < 2 or not isinstance(split_every, Integral): raise ValueError("split_every must be an integer >= 2") # Handle selecting numeric data and associated deprecation warning maybe_warn = False if numeric_only is no_default: if PANDAS_GE_200: numeric_only = False elif PANDAS_GE_150: maybe_warn = True numeric_only = True else: numeric_only = True all_numeric = df._get_numeric_data()._name == df._name if maybe_warn and not all_numeric: warnings.warn( "The default value of numeric_only will be `False` " "in a future version of Dask.", FutureWarning, ) if numeric_only and not all_numeric: df = df._get_numeric_data() if scalar and len(df.columns) != 2: raise ValueError("scalar only valid for 2 column dataframe") token = tokenize(df, min_periods, scalar, split_every) funcname = "corr" if corr else "cov" a = f"{funcname}-chunk-{df._name}" dsk = { (a, i): (_cov_corr_chunk, f, corr) for (i, f) in enumerate(df.__dask_keys__()) } prefix = f"{funcname}-combine-{df._name}-" k = df.npartitions b = a depth = 0 while k > split_every: b = prefix + str(depth) for part_i, inds in enumerate(partition_all(split_every, range(k))): dsk[(b, part_i)] = (_cov_corr_combine, [(a, i) for i in inds], corr) k = part_i + 1 a = b depth += 1 name = f"{funcname}-{token}" dsk[(name, 0)] = ( _cov_corr_agg, [(a, i) for i in range(k)], df.columns, min_periods, corr, scalar, df._meta, ) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[df]) if scalar: return Scalar(graph, name, "f8") meta = make_meta( [(c, "f8") for c in df.columns], index=meta_series_constructor(df)(df.columns), parent_meta=df._meta, ) return new_dd_object(graph, name, meta, (df.columns.min(), df.columns.max())) def _cov_corr_chunk(df, corr=False): """Chunk part of a covariance or correlation computation""" shape = (df.shape[1], df.shape[1]) kwargs = {} if PANDAS_GE_300 else {"copy": False} df = df.astype("float64", **kwargs) sums = np.zeros_like(df.values, shape=shape) counts = np.zeros_like(df.values, shape=shape) for idx in range(len(df.columns)): mask = df.iloc[:, idx].notnull() sums[idx] = df[mask].sum().values counts[idx] = df[mask].count().values # Special case single-row DataFrame cov to avoid warnings from pandas. if df.shape[0] == 1: cov = np.full_like(sums, np.nan) # always an all nan result else: cov = df.cov().values dtype = [("sum", sums.dtype), ("count", counts.dtype), ("cov", cov.dtype)] if corr: with warnings.catch_warnings(record=True): warnings.simplefilter("always") mu = (sums / counts).T m = np.zeros_like(df.values, shape=shape) mask = df.isnull().values for idx in range(len(df.columns)): # Avoid using ufunc.outer (not supported by cupy) mu_discrepancy = ( np.subtract(df.iloc[:, idx].values[:, None], mu[idx][None, :]) ** 2 ) mu_discrepancy[mask] = np.nan m[idx] = np.nansum(mu_discrepancy, axis=0) m = m.T dtype.append(("m", m.dtype)) out = {"sum": sums, "count": counts, "cov": cov * (counts - 1)} if corr: out["m"] = m return out def _cov_corr_combine(data_in, corr=False): data = {"sum": None, "count": None, "cov": None} if corr: data["m"] = None for k in data.keys(): data[k] = [d[k] for d in data_in] data[k] = np.concatenate(data[k]).reshape((len(data[k]),) + data[k][0].shape) sums = np.nan_to_num(data["sum"]) counts = data["count"] cum_sums = np.cumsum(sums, 0) cum_counts = np.cumsum(counts, 0) s1 = cum_sums[:-1] s2 = sums[1:] n1 = cum_counts[:-1] n2 = counts[1:] with np.errstate(invalid="ignore"): d = (s2 / n2) - (s1 / n1) C = np.nansum( (n1 * n2) / (n1 + n2) * (d * d.transpose((0, 2, 1))), 0 ) + np.nansum(data["cov"], 0) out = {"sum": cum_sums[-1], "count": cum_counts[-1], "cov": C} if corr: nobs = np.where(cum_counts[-1], cum_counts[-1], np.nan) mu = cum_sums[-1] / nobs counts_na = np.where(counts, counts, np.nan) m = np.nansum(data["m"] + counts * (sums / counts_na - mu) ** 2, axis=0) out["m"] = m return out def _cov_corr_agg(data, cols, min_periods=2, corr=False, scalar=False, like_df=None): out = _cov_corr_combine(data, corr) counts = out["count"] C = out["cov"] C[counts < min_periods] = np.nan if corr: m2 = out["m"] den = np.sqrt(m2 * m2.T) else: den = np.where(counts, counts, np.nan) - 1 with np.errstate(invalid="ignore", divide="ignore"): mat = C / den if scalar: return float(mat[0, 1]) return (pd.DataFrame if like_df is None else meta_frame_constructor(like_df))( mat, columns=cols, index=cols ) def pd_split(df, p, random_state=None, shuffle=False): """Split DataFrame into multiple pieces pseudorandomly >>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6], ... 'b': [2, 3, 4, 5, 6, 7]}) >>> a, b = pd_split( ... df, [0.5, 0.5], random_state=123, shuffle=True ... ) # roughly 50/50 split >>> a a b 3 4 5 0 1 2 5 6 7 >>> b a b 1 2 3 4 5 6 2 3 4 """ p = list(p) if shuffle: if not isinstance(random_state, np.random.RandomState): random_state = np.random.RandomState(random_state) df = df.sample(frac=1.0, random_state=random_state) index = pseudorandom(len(df), p, random_state) return [df.iloc[index == i] for i in range(len(p))] def _take_last(a, skipna=True): """ take last row (Series) of DataFrame / last value of Series considering NaN. Parameters ---------- a : pd.DataFrame or pd.Series skipna : bool, default True Whether to exclude NaN """ def _last_valid(s): for i in range(1, min(10, len(s) + 1)): val = s.iloc[-i] if not pd.isnull(val): return val else: nonnull = s[s.notna()] if not nonnull.empty: return nonnull.iloc[-1] return None if skipna is False: return a.iloc[-1] else: # take last valid value excluding NaN, NaN location may be different # in each column if is_dataframe_like(a): # create Series from appropriate backend dataframe library series_typ = type(a.iloc[0:1, 0]) if a.empty: return series_typ([], dtype="float") return series_typ( [_last_valid(a.iloc[:, i]) for i in range(len(a.columns))], index=a.columns, ) else: return _last_valid(a) def check_divisions(divisions): if not isinstance(divisions, (list, tuple)): raise ValueError("New division must be list or tuple") divisions = list(divisions) if len(divisions) == 0: raise ValueError("New division must not be empty") if divisions != sorted(divisions): raise ValueError("New division must be sorted") if len(divisions[:-1]) != len(list(unique(divisions[:-1]))): msg = "New division must be unique, except for the last element" raise ValueError(msg) def repartition_divisions(a, b, name, out1, out2, force=False): """dask graph to repartition dataframe by new divisions Parameters ---------- a : tuple old divisions b : tuple, listmypy out2 : str name of new dataframe force : bool, default False Allows the expansion of the existing divisions. If False then the new divisions lower and upper bounds must be the same as the old divisions. Examples -------- >>> from pprint import pprint >>> pprint(repartition_divisions([1, 3, 7], [1, 4, 6, 7], 'a', 'b', 'c')) # doctest: +ELLIPSIS {('b', 0): (, ('a', 0), 1, 3, False), ('b', 1): (, ('a', 1), 3, 4, False), ('b', 2): (, ('a', 1), 4, 6, False), ('b', 3): (, ('a', 1), 6, 7, True), ('c', 0): (, [('b', 0), ('b', 1)]), ('c', 1): ('b', 2), ('c', 2): ('b', 3)} """ check_divisions(b) if len(b) < 2: # minimum division is 2 elements, like [0, 0] raise ValueError("New division must be longer than 2 elements") if force: if a[0] < b[0]: msg = ( "left side of the new division must be equal or smaller " "than old division" ) raise ValueError(msg) if a[-1] > b[-1]: msg = ( "right side of the new division must be equal or larger " "than old division" ) raise ValueError(msg) else: if a[0] != b[0]: msg = "left side of old and new divisions are different" raise ValueError(msg) if a[-1] != b[-1]: msg = "right side of old and new divisions are different" raise ValueError(msg) def _is_single_last_div(x): """Whether last division only contains single label""" return len(x) >= 2 and x[-1] == x[-2] c = [a[0]] d = dict() low = a[0] i, j = 1, 1 # indices for old/new divisions k = 0 # index for temp divisions last_elem = _is_single_last_div(a) # process through old division # left part of new division can be processed in this loop while i < len(a) and j < len(b): if a[i] < b[j]: # tuple is something like: # (methods.boundary_slice, ('from_pandas-#', 0), 3, 4, False)) d[(out1, k)] = (methods.boundary_slice, (name, i - 1), low, a[i], False) low = a[i] i += 1 elif a[i] > b[j]: d[(out1, k)] = (methods.boundary_slice, (name, i - 1), low, b[j], False) low = b[j] j += 1 else: d[(out1, k)] = (methods.boundary_slice, (name, i - 1), low, b[j], False) low = b[j] if len(a) == i + 1 or a[i] < a[i + 1]: j += 1 i += 1 c.append(low) k += 1 # right part of new division can remain if a[-1] < b[-1] or b[-1] == b[-2]: for _j in range(j, len(b)): # always use right-most of old division # because it may contain last element m = len(a) - 2 d[(out1, k)] = (methods.boundary_slice, (name, m), low, b[_j], False) low = b[_j] c.append(low) k += 1 else: # even if new division is processed through, # right-most element of old division can remain if last_elem and i < len(a): d[(out1, k)] = (methods.boundary_slice, (name, i - 1), a[i], a[i], False) k += 1 c.append(a[-1]) # replace last element of tuple with True d[(out1, k - 1)] = d[(out1, k - 1)][:-1] + (True,) i, j = 0, 1 last_elem = _is_single_last_div(c) while j < len(b): tmp = [] while c[i] < b[j]: tmp.append((out1, i)) i += 1 while ( last_elem and c[i] == b[-1] and (b[-1] != b[-2] or j == len(b) - 1) and i < k ): # append if last split is not included tmp.append((out1, i)) i += 1 if len(tmp) == 0: # dummy slice to return empty DataFrame or Series, # which retain original data attributes (columns / name) d[(out2, j - 1)] = (methods.boundary_slice, (name, 0), a[0], a[0], False) elif len(tmp) == 1: d[(out2, j - 1)] = tmp[0] else: if not tmp: raise ValueError( "check for duplicate partitions\nold:\n%s\n\n" "new:\n%s\n\ncombined:\n%s" % (pformat(a), pformat(b), pformat(c)) ) d[(out2, j - 1)] = (methods.concat, tmp) j += 1 return d def repartition_freq(df, freq=None): """Repartition a timeseries dataframe by a new frequency""" if not isinstance(df.divisions[0], pd.Timestamp): raise TypeError("Can only repartition on frequency for timeseries") freq = _map_freq_to_period_start(freq) try: start = df.divisions[0].ceil(freq) except ValueError: start = df.divisions[0] divisions = methods.tolist( pd.date_range(start=start, end=df.divisions[-1], freq=freq) ) if not len(divisions): divisions = [df.divisions[0], df.divisions[-1]] else: divisions.append(df.divisions[-1]) if divisions[0] != df.divisions[0]: divisions = [df.divisions[0]] + divisions return df.repartition(divisions=divisions) def _map_freq_to_period_start(freq): """Ensure that the frequency pertains to the **start** of a period. If e.g. `freq='M'`, then the divisions are: - 2021-31-1 00:00:00 (start of February partition) - 2021-2-28 00:00:00 (start of March partition) - ... but this **should** be: - 2021-2-1 00:00:00 (start of February partition) - 2021-3-1 00:00:00 (start of March partition) - ... Therefore, we map `freq='M'` to `freq='MS'` (same for quarter and year). """ if not isinstance(freq, str): return freq offset = pd.tseries.frequencies.to_offset(freq) offset_type_name = type(offset).__name__ if not offset_type_name.endswith("End"): return freq new_offset = offset_type_name[: -len("End")] + "Begin" try: new_offset_type = getattr(pd.tseries.offsets, new_offset) if "-" in freq: _, anchor = freq.split("-") anchor = "-" + anchor else: anchor = "" n = str(offset.n) if offset.n != 1 else "" return f"{n}{new_offset_type._prefix}{anchor}" except AttributeError: return freq def repartition_size(df, size): """ Repartition dataframe so that new partitions have approximately `size` memory usage each """ if isinstance(size, str): size = parse_bytes(size) size = int(size) mem_usages = df.map_partitions(total_mem_usage, deep=True).compute() # 1. split each partition that is larger than partition_size nsplits = 1 + mem_usages // size if np.any(nsplits > 1): split_name = f"repartition-split-{size}-{tokenize(df)}" df = _split_partitions(df, nsplits, split_name) # update mem_usages to account for the split partitions split_mem_usages = [] for n, usage in zip(nsplits, mem_usages): split_mem_usages.extend([usage / n] * n) mem_usages = pd.Series(split_mem_usages) # 2. now that all partitions are less than size, concat them up to size assert np.all(mem_usages <= size) new_npartitions = list(map(len, iter_chunks(mem_usages, size))) new_partitions_boundaries = np.cumsum(new_npartitions) new_name = f"repartition-{size}-{tokenize(df)}" return _repartition_from_boundaries(df, new_partitions_boundaries, new_name) def total_mem_usage(df, index=True, deep=False): mem_usage = df.memory_usage(index=index, deep=deep) if is_series_like(mem_usage): mem_usage = mem_usage.sum() return mem_usage def repartition_npartitions(df, npartitions): """Repartition dataframe to a smaller number of partitions""" new_name = "repartition-%d-%s" % (npartitions, tokenize(df)) if df.npartitions == npartitions: return df elif df.npartitions > npartitions: npartitions_ratio = df.npartitions / npartitions new_partitions_boundaries = [ int(new_partition_index * npartitions_ratio) for new_partition_index in range(npartitions + 1) ] return _repartition_from_boundaries(df, new_partitions_boundaries, new_name) else: # Drop duplicates in case last partition has same # value for min and max division original_divisions = divisions = pd.Series(df.divisions).drop_duplicates() if df.known_divisions and ( is_datetime64_any_dtype(divisions.dtype) or is_numeric_dtype(divisions.dtype) ): if is_datetime64_any_dtype(divisions.dtype): divisions = divisions.values.astype("float64") if is_series_like(divisions): divisions = divisions.values n = len(divisions) divisions = np.interp( x=np.linspace(0, n, npartitions + 1), xp=np.linspace(0, n, n), fp=divisions, ) if is_datetime64_any_dtype(original_divisions.dtype): divisions = methods.tolist( pd.Series(divisions).astype(original_divisions.dtype) ) elif np.issubdtype(original_divisions.dtype, np.integer): divisions = divisions.astype(original_divisions.dtype) if isinstance(divisions, np.ndarray): divisions = divisions.tolist() divisions = list(divisions) divisions[0] = df.divisions[0] divisions[-1] = df.divisions[-1] # Ensure the computed divisions are unique divisions = list(unique(divisions[:-1])) + [divisions[-1]] return df.repartition(divisions=divisions) else: div, mod = divmod(npartitions, df.npartitions) nsplits = [div] * df.npartitions nsplits[-1] += mod return _split_partitions(df, nsplits, new_name) def _repartition_from_boundaries(df, new_partitions_boundaries, new_name): if not isinstance(new_partitions_boundaries, list): new_partitions_boundaries = list(new_partitions_boundaries) if new_partitions_boundaries[0] > 0: new_partitions_boundaries.insert(0, 0) if new_partitions_boundaries[-1] < df.npartitions: new_partitions_boundaries.append(df.npartitions) dsk = {} for i, (start, end) in enumerate( zip(new_partitions_boundaries, new_partitions_boundaries[1:]) ): dsk[new_name, i] = (methods.concat, [(df._name, j) for j in range(start, end)]) divisions = [df.divisions[i] for i in new_partitions_boundaries] graph = HighLevelGraph.from_collections(new_name, dsk, dependencies=[df]) return new_dd_object(graph, new_name, df._meta, divisions) def _split_partitions(df, nsplits, new_name): """Split a Dask dataframe into new partitions Parameters ---------- df: DataFrame or Series nsplits: List[int] Number of target dataframes for each partition The length of nsplits should be the same as df.npartitions new_name: str See Also -------- repartition_npartitions repartition_size """ if len(nsplits) != df.npartitions: raise ValueError(f"nsplits should have len={df.npartitions}") dsk = {} split_name = f"split-{tokenize(df, nsplits)}" j = 0 for i, k in enumerate(nsplits): if k == 1: dsk[new_name, j] = (df._name, i) j += 1 else: dsk[split_name, i] = (split_evenly, (df._name, i), k) for jj in range(k): dsk[new_name, j] = (getitem, (split_name, i), jj) j += 1 divisions = [None] * (1 + sum(nsplits)) graph = HighLevelGraph.from_collections(new_name, dsk, dependencies=[df]) return new_dd_object(graph, new_name, df._meta, divisions) def repartition(df, divisions=None, force=False): """Repartition dataframe along new divisions Dask.DataFrame objects are partitioned along their index. Often when multiple dataframes interact we need to align these partitionings. The ``repartition`` function constructs a new DataFrame object holding the same data but partitioned on different values. It does this by performing a sequence of ``loc`` and ``concat`` calls to split and merge the previous generation of partitions. Parameters ---------- divisions : list List of partitions to be used force : bool, default False Allows the expansion of the existing divisions. If False then the new divisions lower and upper bounds must be the same as the old divisions. Examples -------- >>> df = df.repartition([0, 5, 10, 20]) # doctest: +SKIP Also works on Pandas objects >>> ddf = dd.repartition(df, [0, 5, 10, 20]) # doctest: +SKIP """ # no-op fastpath for when we already have matching divisions if is_dask_collection(df) and df.divisions == tuple(divisions): return df token = tokenize(df, divisions) if isinstance(df, _Frame): tmp = "repartition-split-" + token out = "repartition-merge-" + token dsk = repartition_divisions( df.divisions, divisions, df._name, tmp, out, force=force ) graph = HighLevelGraph.from_collections(out, dsk, dependencies=[df]) return new_dd_object(graph, out, df._meta, divisions) elif is_dataframe_like(df) or is_series_like(df): name = "repartition-dataframe-" + token from dask.dataframe.utils import shard_df_on_index dfs = shard_df_on_index(df, divisions[1:-1]) dsk = {(name, i): df for i, df in enumerate(dfs)} return new_dd_object(dsk, name, df, divisions) raise ValueError("Data must be DataFrame or Series") def _reduction_chunk(x, aca_chunk=None, **kwargs): o = aca_chunk(x, **kwargs) # Return a dataframe so that the concatenated version is also a dataframe if not is_series_like(o): return o result = o.to_frame().T if o.dtype.kind == "O": # Was coerced to object, so cast back return result.infer_objects() return result def _reduction_combine(x, aca_combine=None, **kwargs): if isinstance(x, list): x = pd.Series(x) o = aca_combine(x, **kwargs) # Return a dataframe so that the concatenated version is also a dataframe return o.to_frame().T if is_series_like(o) else o def _reduction_aggregate(x, aca_aggregate=None, **kwargs): if isinstance(x, list): x = pd.Series(x) return aca_aggregate(x, **kwargs) def idxmaxmin_chunk(x, fn=None, skipna=True, numeric_only=False): numeric_only_kwargs = ( {} if not PANDAS_GE_150 or is_series_like(x) else {"numeric_only": numeric_only} ) minmax = "max" if fn == "idxmax" else "min" if len(x) > 0: idx = getattr(x, fn)(skipna=skipna, **numeric_only_kwargs) value = getattr(x, minmax)(skipna=skipna, **numeric_only_kwargs) else: idx = value = meta_series_constructor(x)([], dtype="i8") if is_series_like(idx): return meta_frame_constructor(x)({"idx": idx, "value": value}) return meta_frame_constructor(x)({"idx": [idx], "value": [value]}) def idxmaxmin_row(x, fn=None, skipna=True): minmax = "max" if fn == "idxmax" else "min" if len(x) > 0: x = x.set_index("idx") # potentially coerced to object, so cast back value = x.value.infer_objects() idx = [getattr(value, fn)(skipna=skipna)] value = [getattr(value, minmax)(skipna=skipna)] else: idx = value = meta_series_constructor(x)([], dtype="i8") return meta_frame_constructor(x)( { "idx": meta_series_constructor(x)(idx, dtype=x.index.dtype), "value": meta_series_constructor(x)(value, dtype=x.dtypes.iloc[0]), } ) def idxmaxmin_combine(x, fn=None, skipna=True): if len(x) <= 1: return x return ( x.groupby(level=0) .apply(idxmaxmin_row, fn=fn, skipna=skipna) .reset_index(level=1, drop=True) ) def idxmaxmin_agg(x, fn=None, skipna=True, scalar=False, numeric_only=no_default): res = idxmaxmin_combine(x, fn, skipna=skipna)["idx"] if len(res) == 0: raise ValueError("attempt to get argmax of an empty sequence") if scalar: return res[0] res.name = None return res def _mode_aggregate(df, dropna): value_count_series = df.sum() max_val = value_count_series.max(skipna=dropna) mode_series = ( value_count_series[value_count_series == max_val] .index.to_series() .sort_values() .reset_index(drop=True) ) return mode_series def _count_aggregate(x): return x.sum().astype("int64") def safe_head(df, n): r = M.head(df, n) if len(r) != n: warnings.warn( f"Insufficient elements for `head`. {n} elements requested, only {len(r)} " "elements available. Try passing larger `npartitions` to `head`." ) return r def maybe_shift_divisions(df, periods, freq): """Maybe shift divisions by periods of size freq Used to shift the divisions for the `shift` method. If freq isn't a fixed size (not anchored or relative), then the divisions are shifted appropriately. Otherwise the divisions are cleared. Parameters ---------- df : dd.DataFrame, dd.Series, or dd.Index periods : int The number of periods to shift. freq : DateOffset, timedelta, or time rule string The frequency to shift by. """ if isinstance(freq, str): freq = pd.tseries.frequencies.to_offset(freq) is_offset = isinstance(freq, pd.DateOffset) if is_offset: if not isinstance(freq, pd.offsets.Tick): # Can't infer divisions on relative or anchored offsets, as # divisions may now split identical index value. # (e.g. index_partitions = [[1, 2, 3], [3, 4, 5]]) return df.clear_divisions() if df.known_divisions: divs = pd.Series(range(len(df.divisions)), index=df.divisions) divisions = divs.shift(periods, freq=freq).index return df.__class__(df.dask, df._name, df._meta, divisions) return df @wraps(pd.to_datetime) def to_datetime(arg, meta=None, **kwargs): tz_kwarg = {"tz": "utc"} if kwargs.get("utc") else {} if meta is None: if isinstance(arg, Index): meta = get_meta_library(arg).DatetimeIndex([], **tz_kwarg) meta.name = arg.name elif not (is_dataframe_like(arg) or is_series_like(arg)): raise NotImplementedError( "dask.dataframe.to_datetime does not support " "non-index-able arguments (like scalars)" ) else: meta = meta_series_constructor(arg)([pd.Timestamp("2000", **tz_kwarg)]) meta.index = meta.index.astype(arg.index.dtype) meta.index.name = arg.index.name if PANDAS_GE_200 and "infer_datetime_format" in kwargs: warnings.warn( "The argument 'infer_datetime_format' is deprecated and will be removed in a future version. " "A strict version of it is now the default, see " "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. " "You can safely remove this argument.", UserWarning, ) kwargs.pop("infer_datetime_format") return map_partitions(get_meta_library(arg).to_datetime, arg, meta=meta, **kwargs) @wraps(pd.to_timedelta) def to_timedelta(arg, unit=None, errors="raise"): meta = meta_series_constructor(arg)([pd.Timedelta(1, unit=unit)]) return map_partitions(pd.to_timedelta, arg, unit=unit, errors=errors, meta=meta) if hasattr(pd, "isna"): @wraps(pd.isna) def isna(arg): return map_partitions(pd.isna, arg) def _repr_data_series(s, index): """A helper for creating the ``_repr_data`` property""" npartitions = len(index) - 1 if isinstance(s.dtype, pd.CategoricalDtype): if has_known_categories(s): dtype = "category[known]" else: dtype = "category[unknown]" else: dtype = str(s.dtype) return pd.Series([dtype] + ["..."] * npartitions, index=index, name=s.name) def has_parallel_type(x): """Does this object have a dask dataframe equivalent?""" return get_parallel_type(x) is not Scalar def new_dd_object(dsk, name, meta, divisions, parent_meta=None): """Generic constructor for dask.dataframe objects. Decides the appropriate output class based on the type of `meta` provided. """ if has_parallel_type(meta): return get_parallel_type(meta)(dsk, name, meta, divisions) elif is_arraylike(meta) and meta.shape: import dask.array as da chunks = ((np.nan,) * (len(divisions) - 1),) + tuple( (d,) for d in meta.shape[1:] ) if len(chunks) > 1: if isinstance(dsk, HighLevelGraph): layer = dsk.layers[name] else: # dask-expr provides a dict only layer = dsk if isinstance(layer, Blockwise): layer.new_axes["j"] = chunks[1][0] layer.output_indices = layer.output_indices + ("j",) else: suffix = (0,) * (len(chunks) - 1) for i in range(len(chunks[0])): layer[(name, i) + suffix] = layer.pop((name, i)) return da.Array(dsk, name=name, chunks=chunks, dtype=meta.dtype) else: return get_parallel_type(meta)(dsk, name, meta, divisions) def partitionwise_graph(func, layer_name, *args, **kwargs): """ Apply a function partition-wise across arguments to create layer of a graph This applies a function, ``func``, in an embarrassingly parallel fashion across partitions/chunks in the provided arguments. It handles Dataframes, Arrays, and scalars smoothly, and relies on the ``blockwise`` machinery to provide a nicely symbolic graph. It is most commonly used in other graph-building functions to create the appropriate layer of the resulting dataframe. Parameters ---------- func: callable layer_name: str Descriptive name for the operation. Used as the output name in the resulting ``Blockwise`` graph layer. *args: **kwargs: Returns ------- out: Blockwise graph Examples -------- >>> subgraph = partitionwise_graph(function, x, y, z=123) # doctest: +SKIP >>> layer = partitionwise_graph(function, df, x, z=123) # doctest: +SKIP >>> graph = HighLevelGraph.from_collections(name, layer, dependencies=[df, x]) # doctest: +SKIP >>> result = new_dd_object(graph, name, metadata, df.divisions) # doctest: +SKIP See Also -------- map_partitions """ pairs = [] numblocks = {} for arg in args: if isinstance(arg, _Frame): pairs.extend([arg._name, "i"]) numblocks[arg._name] = (arg.npartitions,) elif isinstance(arg, Scalar): pairs.extend([arg._name, "i"]) numblocks[arg._name] = (1,) elif isinstance(arg, Array): if arg.ndim == 1: pairs.extend([arg.name, "i"]) elif arg.ndim == 0: pairs.extend([arg.name, ""]) elif arg.ndim == 2: pairs.extend([arg.name, "ij"]) else: raise ValueError("Can't add multi-dimensional array to dataframes") numblocks[arg._name] = arg.numblocks elif isinstance(arg, BlockwiseDep): if len(arg.numblocks) == 1: pairs.extend([arg, "i"]) elif len(arg.numblocks) == 2: pairs.extend([arg, "ij"]) else: raise ValueError( f"BlockwiseDep arg {arg!r} has {len(arg.numblocks)} dimensions; only 1 or 2 are supported." ) else: pairs.extend([arg, None]) return blockwise( func, layer_name, "i", *pairs, numblocks=numblocks, concatenate=True, **kwargs ) def meta_warning(df): """ Provide an informative message when the user is asked to provide metadata """ if is_dataframe_like(df): meta_str = {k: str(v) for k, v in df.dtypes.to_dict().items()} elif is_series_like(df): meta_str = (df.name, str(df.dtype)) else: meta_str = None msg = ( "\nYou did not provide metadata, so Dask is running your " "function on a small dataset to guess output types. " "It is possible that Dask will guess incorrectly.\n" "To provide an explicit output types or to silence this message, " "please provide the `meta=` keyword, as described in the map or " "apply function that you are using." ) if meta_str: msg += ( "\n" " Before: .apply(func)\n" " After: .apply(func, meta=%s)\n" % str(meta_str) ) return msg def prefix_reduction(f, ddf, identity, **kwargs): """Computes the prefix sums of f on df If df has partitions [P1, P2, ..., Pn], then returns the DataFrame with partitions [f(identity, P1), f(f(identity, P1), P2), f(f(f(identity, P1), P2), P3), ...] Parameters ---------- f : callable an associative function f ddf : dd.DataFrame identity : pd.DataFrame an identity element of f, that is f(identity, df) = f(df, identity) = df """ dsk = dict() name = "prefix_reduction-" + tokenize(f, ddf, identity, **kwargs) meta = ddf._meta n = len(ddf.divisions) - 1 divisions = [None] * (n + 1) N = 1 while N < n: N *= 2 for i in range(n): dsk[(name, i, 1, 0)] = (apply, f, [(ddf._name, i), identity], kwargs) for i in range(n, N): dsk[(name, i, 1, 0)] = identity d = 1 while d < N: for i in range(0, N, 2 * d): dsk[(name, i + 2 * d - 1, 2 * d, 0)] = ( apply, f, [(name, i + d - 1, d, 0), (name, i + 2 * d - 1, d, 0)], kwargs, ) d *= 2 dsk[(name, N - 1, N, 1)] = identity while d > 1: d //= 2 for i in range(0, N, 2 * d): dsk[(name, i + d - 1, d, 1)] = (name, i + 2 * d - 1, 2 * d, 1) dsk[(name, i + 2 * d - 1, d, 1)] = ( apply, f, [(name, i + 2 * d - 1, 2 * d, 1), (name, i + d - 1, d, 0)], kwargs, ) for i in range(n): dsk[(name, i)] = (apply, f, [(name, i, 1, 1), identity], kwargs) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[ddf]) return new_dd_object(graph, name, meta, divisions) def suffix_reduction(f, ddf, identity, **kwargs): """Computes the suffix sums of f on df If df has partitions [P1, P2, ..., Pn], then returns the DataFrame with partitions [f(P1, f(P2, ...f(Pn, identity)...)), f(P2, ...f(Pn, identity)...), ...f(Pn, identity)..., ...] Parameters ---------- f : callable an associative function f ddf : dd.DataFrame identity : pd.DataFrame an identity element of f, that is f(identity, df) = f(df, identity) = df kwargs : ?? keyword arguments of f ?? """ dsk = dict() name = "suffix_reduction-" + tokenize(f, ddf, identity, **kwargs) meta = ddf._meta n = len(ddf.divisions) - 1 divisions = [None] * (n + 1) N = 1 while N < n: N *= 2 for i in range(n): dsk[(name, i, 1, 0)] = (apply, f, [(ddf._name, n - 1 - i), identity], kwargs) for i in range(n, N): dsk[(name, i, 1, 0)] = identity d = 1 while d < N: for i in range(0, N, 2 * d): dsk[(name, i + 2 * d - 1, 2 * d, 0)] = ( apply, f, [(name, i + 2 * d - 1, d, 0), (name, i + d - 1, d, 0)], kwargs, ) d *= 2 dsk[(name, N - 1, N, 1)] = identity while d > 1: d //= 2 for i in range(0, N, 2 * d): dsk[(name, i + d - 1, d, 1)] = (name, i + 2 * d - 1, 2 * d, 1) dsk[(name, i + 2 * d - 1, d, 1)] = ( apply, f, [(name, i + d - 1, d, 0), (name, i + 2 * d - 1, 2 * d, 1)], kwargs, ) for i in range(n): dsk[(name, i)] = (apply, f, [(name, n - 1 - i, 1, 1), identity], kwargs) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[ddf]) return new_dd_object(graph, name, meta, divisions) def mapseries(base_chunk, concat_map): return base_chunk.map(concat_map) def mapseries_combine(index, concat_result): final_series = concat_result.sort_index() final_series = index.to_series().map(final_series) return final_series def series_map(base_series, map_series): npartitions = base_series.npartitions split_out = map_series.npartitions dsk = {} base_token_key = tokenize(base_series, split_out) base_split_prefix = f"base-split-{base_token_key}" base_shard_prefix = f"base-shard-{base_token_key}" for i, key in enumerate(base_series.__dask_keys__()): dsk[(base_split_prefix, i)] = (hash_shard, key, split_out) for j in range(split_out): dsk[(base_shard_prefix, 0, i, j)] = (getitem, (base_split_prefix, i), j) map_token_key = tokenize(map_series) map_split_prefix = f"map-split-{map_token_key}" map_shard_prefix = f"map-shard-{map_token_key}" for i, key in enumerate(map_series.__dask_keys__()): dsk[(map_split_prefix, i)] = ( hash_shard, key, split_out, split_out_on_index, None, ) for j in range(split_out): dsk[(map_shard_prefix, 0, i, j)] = (getitem, (map_split_prefix, i), j) token_key = tokenize(base_series, map_series) map_prefix = f"map-series-{token_key}" for i in range(npartitions): for j in range(split_out): dsk[(map_prefix, i, j)] = ( mapseries, (base_shard_prefix, 0, i, j), (_concat, [(map_shard_prefix, 0, k, j) for k in range(split_out)]), ) final_prefix = f"map-series-combine-{token_key}" for i, key in enumerate(base_series.index.__dask_keys__()): dsk[(final_prefix, i)] = ( mapseries_combine, key, (_concat, [(map_prefix, i, j) for j in range(split_out)]), ) meta = map_series._meta.copy() meta.index = base_series._meta.index meta = make_meta(meta) dependencies = [base_series, map_series, base_series.index] graph = HighLevelGraph.from_collections( final_prefix, dsk, dependencies=dependencies ) divisions = list(base_series.divisions) return new_dd_object(graph, final_prefix, meta, divisions) def _convert_to_numeric(series, skipna): if PANDAS_GE_200: if skipna: return series.dropna().astype("i8") # series.view("i8") with pd.NaT produces -9223372036854775808 is why we need to do this return series.astype("i8").mask(series.isnull(), np.nan) else: view = "_view" if isinstance(series, Series) else "view" if skipna: return getattr(series.dropna(), view)("i8") # series.view("i8") with pd.NaT produces -9223372036854775808 is why we need to do this return getattr(series, view)("i8").mask(series.isnull(), np.nan) def _sqrt_and_convert_to_timedelta(partition, axis, dtype=None, *args, **kwargs): if axis == 1: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", category=RuntimeWarning, message="invalid value encountered in cast", ) return pd.to_timedelta(M.std(partition, *args, axis=axis, **kwargs)) is_df_like, time_cols = kwargs["is_df_like"], kwargs["time_cols"] sqrt = np.sqrt(partition) if not is_df_like: return pd.to_timedelta(sqrt) time_col_mask = sqrt.index.isin(time_cols) matching_vals = sqrt[time_col_mask] if len(time_cols) > 0: sqrt = sqrt.astype(object) for time_col, matching_val in zip(time_cols, matching_vals): sqrt[time_col] = pd.to_timedelta(matching_val) if dtype is not None: sqrt = sqrt.astype(dtype) return sqrt def _raise_if_not_series_or_dataframe(x, funcname): """ Utility function to raise an error if an object is not a Series or DataFrame """ if not is_series_like(x) and not is_dataframe_like(x): raise NotImplementedError( "`%s` is only supported with objects that are Dataframes or Series" % funcname ) def _getattr_numeric_only(*args, _dask_method_name, **kwargs): with check_numeric_only_deprecation(): return getattr(M, _dask_method_name)(*args, **kwargs)