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_210,
PANDAS_GE_300,
PANDAS_VERSION,
check_convert_dtype_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
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):
if numeric_only is no_default:
numeric_only = False
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."
)
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_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)
):
# 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<size-ag..., dtype=int64>
"""
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()
np.int64(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):
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: bool = True,
):
axis = self._validate_axis(axis, none_is_zero=none_is_zero) # type: ignore
if has_keyword(getattr(self._meta_nonempty, name), "numeric_only"):
numeric_only_kwargs = {"numeric_only": numeric_only}
else:
numeric_only_kwargs = {}
meta = getattr(self._meta_nonempty, name)(
axis=axis, skipna=skipna, **numeric_only_kwargs
)
token = self._token_prefix + name # type: ignore
if axis == 1:
result = self.map_partitions(
getattr(M, name),
meta=meta,
token=token,
skipna=skipna,
axis=axis,
**numeric_only_kwargs,
)
return handle_out(out, result)
else:
result = self.reduction(
getattr(M, name),
meta=meta,
token=token,
skipna=skipna,
axis=axis,
split_every=split_every,
**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):
return self._reduction_agg(
"max",
axis=axis,
skipna=skipna,
split_every=split_every,
out=out,
numeric_only=numeric_only,
none_is_zero=False,
)
@_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):
return self._reduction_agg(
"min",
axis=axis,
skipna=skipna,
split_every=split_every,
out=out,
numeric_only=numeric_only,
none_is_zero=False,
)
@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,
):
axis = self._validate_axis(axis, none_is_zero=False)
_raise_if_object_series(self, "mean")
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 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_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 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_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 axis is None:
raise ValueError("`axis=None` isn't currently supported for `skew`.")
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 axis is None:
raise ValueError("`axis=None` isn't currently supported for `kurtosis`.")
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")
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)
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(
M.quantile,
self,
q,
axis,
token=keyname,
enforce_metadata=False,
**numeric_kwargs,
meta=(q, "f8"),
parent_meta=self._meta,
)
else:
_raise_if_object_series(self, "quantile")
num = self._get_numeric_data() if numeric_only is True 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,
):
if self._meta.ndim == 1:
meta = self._meta_nonempty.describe(
percentiles=percentiles,
include=include,
exclude=exclude,
)
output = self._describe_1d(
self, split_every, percentiles, percentiles_method
)
output._meta = meta
return output
elif (include is None) and (exclude is None):
_include = [np.number, np.timedelta64]
_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]
_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,
)
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)
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",
):
if is_bool_dtype(data._meta):
return self._describe_nonnumeric_1d(data, split_every=split_every)
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):
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)
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):
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),
]
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)
meta = data._meta_nonempty.describe()
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
)
@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)
[docs]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<size-ag..., dtype=int64>,)
"""
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"),
)
[docs] 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
[docs] @derived_from(pd.Series)
def round(self, decimals=0):
return elemwise(M.round, self, decimals)
[docs] @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
[docs] 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 _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
@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)
[docs] @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,
)
[docs] @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)
[docs] @derived_from(pd.Series)
def explode(self):
meta = self._meta.explode()
return self.map_partitions(M.explode, meta=meta, enforce_metadata=False)
[docs] 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,
)
[docs] @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
[docs] @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,
)
[docs] @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,
)
[docs] @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,
)
[docs] @derived_from(pd.Series)
def isin(self, values):
# Added just to get the different docstring for Series
return super().isin(values)
[docs] @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)
[docs] @derived_from(pd.Series)
def dropna(self):
return self.map_partitions(M.dropna, enforce_metadata=False)
[docs] @derived_from(pd.Series)
def between(self, left, right, inclusive="both"):
return self.map_partitions(
M.between, left=left, right=right, inclusive=inclusive
)
[docs] @_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}),
)
[docs] @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)
[docs] 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)
[docs] @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))
[docs] @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))
[docs] @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)
[docs] @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
)
[docs] @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,
)
[docs] @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)
[docs] @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
@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)
[docs]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)
[docs] 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
[docs] @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,
)
[docs] @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,
)
[docs] def count(self, split_every=False):
return self.reduction(
methods.index_count,
np.sum,
token="index-count",
meta=int,
split_every=split_every,
)
[docs] @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)
[docs] @derived_from(pd.Index)
def to_series(self):
return self.map_partitions(
M.to_series,
meta=self._meta.to_series(),
transform_divisions=False,
)
[docs] @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,
)
[docs] @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
@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
[docs] @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",
)
[docs] @_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)
)
[docs]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()
[docs] 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
[docs] @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]
[docs] 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,
)
[docs] 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,
)
[docs] @derived_from(pd.DataFrame)
def pop(self, item):
out = self[item]
del self[item]
return out
[docs] @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,
)
[docs] @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,
)
[docs] @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
)
[docs] @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
[docs] @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)
[docs] 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)
[docs] @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)
[docs] @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}),
)
[docs] @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)}")
[docs] @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
[docs] @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)
[docs] 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)
[docs] 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)
[docs] @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)
[docs] @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"
)
[docs] @_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,
)
[docs] @_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,
)
[docs] @derived_from(pd.DataFrame)
def iterrows(self):
for i in range(self.npartitions):
df = self.get_partition(i).compute()
yield from df.iterrows()
[docs] @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)
[docs] @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))
[docs] @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)
[docs] @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)
[docs] @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))
[docs] @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
[docs] @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
)
[docs] @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
)
[docs] 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__}")
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)
[docs] @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
[docs] 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
)
[docs] 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,
)
[docs] 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
[docs] @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
)
[docs] @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 <https://github.com/dask/dask/pull/6981>
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):
return func(*_extract_meta(args, True), **_extract_meta(kwargs, True))
[docs]@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
if numeric_only is no_default:
numeric_only = False
all_numeric = df._get_numeric_data()._name == df._name
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":