API Reference — Dask documentation (original) (raw)

API Reference

Dask APIs generally follow from upstream APIs:

Additionally, Dask has its own functions to start computations, persist data in memory, check progress, and so forth that complement the APIs above. These more general Dask functions are described below:

compute(*args[, traverse, optimize_graph, ...]) Compute several dask collections at once.
is_dask_collection(x) Returns True if x is a dask collection.
optimize(*args[, traverse]) Optimize several dask collections at once.
persist(*args[, traverse, optimize_graph, ...]) Persist multiple Dask collections into memory
visualize(*args[, filename, traverse, ...]) Visualize several dask graphs simultaneously.

These functions work with any scheduler. More advanced operations are available when using the newer scheduler and starting adask.distributed.Client (which, despite its name, runs nicely on a single machine). This API provides the ability to submit, cancel, and track work asynchronously, and includes many functions for complex inter-task workflows. These are not necessary for normal operation, but can be useful for real-time or advanced operation.

This more advanced API is available in the Dask distributed documentation

dask.annotate(**annotations: Any) → collections.abc.Iterator[None][source]

Context Manager for setting HighLevelGraph Layer annotations.

Annotations are metadata or soft constraints associated with tasks that dask schedulers may choose to respect: They signal intent without enforcing hard constraints. As such, they are primarily designed for use with the distributed scheduler.

Almost any object can serve as an annotation, but small Python objects are preferred, while large objects such as NumPy arrays are discouraged.

Callables supplied as an annotation should take a single key argument and produce the appropriate annotation. Individual task keys in the annotated collection are supplied to the callable.

Parameters

**annotationskey-value pairs

Examples

All tasks within array A should have priority 100 and be retried 3 times on failure.

import dask import dask.array as da with dask.annotate(priority=100, retries=3): ... A = da.ones((10000, 10000))

Prioritise tasks within Array A on flattened block ID.

nblocks = (10, 10) with dask.annotate(priority=lambda k: k[1]*nblocks[1] + k[2]): ... A = da.ones((1000, 1000), chunks=(100, 100))

Annotations may be nested.

with dask.annotate(priority=1): ... with dask.annotate(retries=3): ... A = da.ones((1000, 1000)) ... B = A + 1

dask.get_annotations() → dict[str, Any][source]

Get current annotations.

Returns

Dict of all current annotations

dask.compute(*args, traverse=True, optimize_graph=True, scheduler=None, get=None, **kwargs)[source]

Compute several dask collections at once.

Parameters

argsobject

Any number of objects. If it is a dask object, it’s computed and the result is returned. By default, python builtin collections are also traversed to look for dask objects (for more information see thetraverse keyword). Non-dask arguments are passed through unchanged.

traversebool, optional

By default dask traverses builtin python collections looking for dask objects passed to compute. For large collections this can be expensive. If none of the arguments contain any dask objects, settraverse=False to avoid doing this traversal.

schedulerstring, optional

Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.

optimize_graphbool, optional

If True [default], the optimizations for each collection are applied before computation. Otherwise the graph is run as is. This can be useful for debugging.

getNone

Should be left to None The get= keyword has been removed.

kwargs

Extra keywords to forward to the scheduler function.

Examples

import dask import dask.array as da a = da.arange(10, chunks=2).sum() b = da.arange(10, chunks=2).mean() dask.compute(a, b) (np.int64(45), np.float64(4.5))

By default, dask objects inside python collections will also be computed:

dask.compute({'a': a, 'b': b, 'c': 1}) ({'a': np.int64(45), 'b': np.float64(4.5), 'c': 1},)

dask.is_dask_collection(x) → bool[source]

Returns True if x is a dask collection.

Parameters

xAny

Object to test.

Returns

resultbool

True if x is a Dask collection.

Notes

The DaskCollection typing.Protocol implementation defines a Dask collection as a class that returns a Mapping from the__dask_graph__ method. This helper function existed before the implementation of the protocol.

dask.optimize(*args, traverse=True, **kwargs)[source]

Optimize several dask collections at once.

Returns equivalent dask collections that all share the same merged and optimized underlying graph. This can be useful if converting multiple collections to delayed objects, or to manually apply the optimizations at strategic points.

Note that in most cases you shouldn’t need to call this function directly.

Warning:

This function triggers a materialization of the collections and looses any annotations attached to HLG layers.

Parameters

*argsobjects

Any number of objects. If a dask object, its graph is optimized and merged with all those of all other dask objects before returning an equivalent dask collection. Non-dask arguments are passed through unchanged.

traversebool, optional

By default dask traverses builtin python collections looking for dask objects passed to optimize. For large collections this can be expensive. If none of the arguments contain any dask objects, settraverse=False to avoid doing this traversal.

optimizationslist of callables, optional

Additional optimization passes to perform.

**kwargs

Extra keyword arguments to forward to the optimization passes.

Examples

import dask import dask.array as da a = da.arange(10, chunks=2).sum() b = da.arange(10, chunks=2).mean() a2, b2 = dask.optimize(a, b)

a2.compute() == a.compute() np.True_ b2.compute() == b.compute() np.True_

dask.persist(*args, traverse=True, optimize_graph=True, scheduler=None, **kwargs)[source]

Persist multiple Dask collections into memory

This turns lazy Dask collections into Dask collections with the same metadata, but now with their results fully computed or actively computing in the background.

For example a lazy dask.array built up from many lazy calls will now be a dask.array of the same shape, dtype, chunks, etc., but now with all of those previously lazy tasks either computed in memory as many small numpy.array(in the single-machine case) or asynchronously running in the background on a cluster (in the distributed case).

This function operates differently if a dask.distributed.Client exists and is connected to a distributed scheduler. In this case this function will return as soon as the task graph has been submitted to the cluster, but before the computations have completed. Computations will continue asynchronously in the background. When using this function with the single machine scheduler it blocks until the computations have finished.

When using Dask on a single machine you should ensure that the dataset fits entirely within memory.

Parameters

*args: Dask collections

schedulerstring, optional

Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.

traversebool, optional

By default dask traverses builtin python collections looking for dask objects passed to persist. For large collections this can be expensive. If none of the arguments contain any dask objects, settraverse=False to avoid doing this traversal.

optimize_graphbool, optional

If True [default], the graph is optimized before computation. Otherwise the graph is run as is. This can be useful for debugging.

**kwargs

Extra keywords to forward to the scheduler function.

Returns

New dask collections backed by in-memory data

Examples

df = dd.read_csv('/path/to/*.csv')
df = df[df.name == 'Alice']
df['in-debt'] = df.balance < 0
df = df.persist() # triggers computation

df.value().min() # future computations are now fast
-10 df.value().max()
100

from dask import persist # use persist function on multiple collections a, b = persist(a, b)

dask.visualize(*args, filename='mydask', traverse=True, optimize_graph=False, maxval=None, engine: Optional[Literal['cytoscape', 'ipycytoscape', 'graphviz']] = None, **kwargs)[source]

Visualize several dask graphs simultaneously.

Requires graphviz to be installed. All options that are not the dask graph(s) should be passed as keyword arguments.

Parameters

argsobject

Any number of objects. If it is a dask collection (for example, a dask DataFrame, Array, Bag, or Delayed), its associated graph will be included in the output of visualize. By default, python builtin collections are also traversed to look for dask objects (for more information see the traverse keyword). Arguments lacking an associated graph will be ignored.

filenamestr or None, optional

The name of the file to write to disk. If the provided filenamedoesn’t include an extension, ‘.png’ will be used by default. If filename is None, no file will be written, and we communicate with dot using only pipes.

format{‘png’, ‘pdf’, ‘dot’, ‘svg’, ‘jpeg’, ‘jpg’}, optional

Format in which to write output file. Default is ‘png’.

traversebool, optional

By default, dask traverses builtin python collections looking for dask objects passed to visualize. For large collections this can be expensive. If none of the arguments contain any dask objects, settraverse=False to avoid doing this traversal.

optimize_graphbool, optional

If True, the graph is optimized before rendering. Otherwise, the graph is displayed as is. Default is False.

color{None, ‘order’, ‘ages’, ‘freed’, ‘memoryincreases’, ‘memorydecreases’, ‘memorypressure’}, optional

Options to color nodes. colormap:

maxval{int, float}, optional

Maximum value for colormap to normalize form 0 to 1.0. Default is Nonewill make it the max number of values

collapse_outputsbool, optional

Whether to collapse output boxes, which often have empty labels. Default is False.

verbosebool, optional

Whether to label output and input boxes even if the data aren’t chunked. Beware: these labels can get very long. Default is False.

engine{“graphviz”, “ipycytoscape”, “cytoscape”}, optional.

The visualization engine to use. If not provided, this checks the dask config value “visualization.engine”. If that is not set, it tries to import graphvizand ipycytoscape, using the first one to succeed.

**kwargs

Additional keyword arguments to forward to the visualization engine.

Returns

resultIPython.display.Image, IPython.display.SVG, or None

See dask.dot.dot_graph for more information.

See also

dask.dot.dot_graph

Notes

For more information on optimization see here:

https://docs.dask.org/en/latest/optimize.html

Examples

x.visualize(filename='dask.pdf')
x.visualize(filename='dask.pdf', color='order')

Datasets

Dask has a few helpers for generating demo datasets

dask.datasets.make_people(npartitions=10, records_per_partition=1000, seed=None, locale='en')[source]

Make a dataset of random people

This makes a Dask Bag with dictionary records of randomly generated people. This requires the optional library mimesis to generate records.

Parameters

npartitionsint

Number of partitions

records_per_partitionint

Number of records in each partition

seedint, (optional)

Random seed

localestr

Language locale, like ‘en’, ‘fr’, ‘zh’, or ‘ru’

Returns

b: Dask Bag

dask.datasets.timeseries(start='2000-01-01', end='2000-01-31', freq='1s', partition_freq='1D', dtypes=None, seed=None, **kwargs)[source]

Create timeseries dataframe with random data

Parameters

startdatetime (or datetime-like string)

Start of time series

enddatetime (or datetime-like string)

End of time series

dtypesdict (optional)

Mapping of column names to types. Valid types include {float, int, str, ‘category’}

freqstring

String like ‘2s’ or ‘1H’ or ‘12W’ for the time series frequency

partition_freqstring

String like ‘1M’ or ‘2Y’ to divide the dataframe into partitions

seedint (optional)

Randomstate seed

kwargs:

Keywords to pass down to individual column creation functions. Keywords should be prefixed by the column name and then an underscore.

Examples

import dask df = dask.datasets.timeseries() df.head()
timestamp id name x y 2000-01-01 00:00:00 967 Jerry -0.031348 -0.040633 2000-01-01 00:00:01 1066 Michael -0.262136 0.307107 2000-01-01 00:00:02 988 Wendy -0.526331 0.128641 2000-01-01 00:00:03 1016 Yvonne 0.620456 0.767270 2000-01-01 00:00:04 998 Ursula 0.684902 -0.463278 df = dask.datasets.timeseries( ... '2000', '2010', ... freq='2h', partition_freq='1D', seed=1, # data frequency ... dtypes={'value': float, 'name': str, 'id': int}, # data types ... id_lam=1000 # control number of items in id column ... )

Datasets with defined specs

The following helpers are still experimental:

dask.dataframe.io.demo.with_spec(spec: dask.dataframe.io.demo.DatasetSpec, seed: int | None = None)[source]

Generate a random dataset according to provided spec

Parameters

specDatasetSpec

Specify all the parameters of the dataset

seed: int (optional)

Randomstate seed

Notes

This API is still experimental, and will likely change in the future

Examples

from dask.dataframe.io.demo import ColumnSpec, DatasetSpec, with_spec ddf = with_spec( ... DatasetSpec( ... npartitions=10, ... nrecords=10_000, ... column_specs=[ ... ColumnSpec(dtype=int, number=2, prefix="p"), ... ColumnSpec(dtype=int, number=2, prefix="n", method="normal"), ... ColumnSpec(dtype=float, number=2, prefix="f"), ... ColumnSpec(dtype=str, prefix="s", number=2, random=True, length=10), ... ColumnSpec(dtype="category", prefix="c", choices=["Y", "N"]), ... ], ... ), seed=42) ddf.head(10)
p1 p2 n1 n2 f1 f2 s1 s2 c1 0 1002 972 -811 20 0.640846 -0.176875 L#h98#}J`? _8C607/:6e N 1 985 982 -1663 -777 0.790257 0.792796 u:XI3,omoZ w@ /d)'-@ N 2 947 970 799 -269 0.740869 -0.118413 O$dnwCuq\ !WtSe+(;#9 Y 3 1003 983 1133 521 -0.987459 0.278154 j+Qr_2{XG& &XV7cy$y1T Y 4 1017 1049 826 5 -0.875667 -0.744359 bJ3E-{:o {+jC).?vK+ Y 5 984 1017 -492 -399 0.748181 0.293761 ~zUNHNgD"! yuEkXeVot| Y 6 992 1027 -856 67 -0.125132 -0.234529 j.7z;o]Gc9 g|Fi5*}Y92 Y 7 1011 974 762 -1223 0.471696 0.937935 yT?jN/-u] JhEB[W-}^$ N 8 984 974 856 74 0.109963 0.367864 _j"&@ i&;/ OYXQ)w{hoH N 9 1030 1001 -792 -262 0.435587 -0.647970 Pmrwl{{|.K 3UTqM$86Sg N

The ColumnSpec class

class dask.dataframe.io.demo.ColumnSpec(prefix: str | None = None, dtype: str | type | None = None, number: int = 1, nunique: int | None = None, choices: list = , low: int | None = None, high: int | None = None, length: int | None = None, random: bool = False, method: str | None = None, args: tuple[typing.Any, ...] = , kwargs: dict[str, typing.Any] = )[source]

Bases: object

Encapsulates properties of a family of columns with the same dtype. Different method can be specified for integer dtype (“poisson”, “uniform”, “binomial”, etc.)

Notes

This API is still experimental, and will likely change in the future

args_: tuple[Any, ...]_

Args to pass into the method

choices_: list_

For a “category” or str column, list of possible values

dtype_: str | type | None_ = None

Column data type. Only supports numpy dtypes

high_: int | None_ = None

For an int column, high end of range

kwargs_: dict[str, Any]_

Any other kwargs to pass into the method

length_: int | None_ = None

For a str or “category” column with random=True, how large a string to generate

low_: int | None_ = None

Start value for an int column. Optional if random=True, since randint doesn’t accept high and low.

method_: str | None_ = None

For an int column, method to use when generating the value, such as “poisson”, “uniform”, “binomial”. Default “poisson”. Delegates to the same method of RandomState

number_: int_ = 1

How many columns to create with these properties. Default 1. If more than one columns are specified, they will be numbered: “int1”, “int2”, etc.

nunique_: int | None_ = None

For a “category” column, how many unique categories to generate

prefix_: str | None_ = None

Column prefix. If not specified, will default to str(dtype)

random_: bool_ = False

For an int column, whether to use randint. For a string column produces a random string of specified length

The RangeIndexSpec class

class dask.dataframe.io.demo.RangeIndexSpec(dtype: str | type = <class 'int'>, step: int = 1)[source]

Bases: object

Properties of the dataframe RangeIndex

Notes

This API is still experimental, and will likely change in the future

dtype

Index dtype

alias of int

step_: int_ = 1

Step for a RangeIndex

The DatetimeIndexSpec class

class dask.dataframe.io.demo.DatetimeIndexSpec(dtype: str | type = <class 'int'>, start: str | None = None, freq: str = '1H', partition_freq: str | None = None)[source]

Bases: object

Properties of the dataframe DatetimeIndex

Notes

This API is still experimental, and will likely change in the future

dtype

Index dtype

alias of int

freq_: str_ = '1H'

Frequency for the index (“1H”, “1D”, etc.)

partition_freq_: str | None_ = None

Partition frequency (“1D”, “1M”, etc.)

start_: str | None_ = None

First value of the index

The DatasetSpec class

class dask.dataframe.io.demo.DatasetSpec(npartitions: int = 1, nrecords: int = 1000, index_spec: dask.dataframe.io.demo.RangeIndexSpec | dask.dataframe.io.demo.DatetimeIndexSpec = , column_specs: list[dask.dataframe.io.demo.ColumnSpec] = )[source]

Bases: object

Defines a dataset with random data, such as which columns and data types to generate

Notes

This API is still experimental, and will likely change in the future

column_specs_: list[dask.dataframe.io.demo.ColumnSpec]_

List of column definitions

index_spec_: dask.dataframe.io.demo.RangeIndexSpec | dask.dataframe.io.demo.DatetimeIndexSpec_

Properties of the index

npartitions_: int_ = 1

How many partitions generate in the dataframe. If the dataframe has a DatetimeIndex, specify its partition_freq instead

nrecords_: int_ = 1000

Total number of records to generate

Utilities

Dask has some public utility methods. These are primarily used for parsing configuration values.

dask.utils.apply(func, args, kwargs=None)[source]

Apply a function given its positional and keyword arguments.

Equivalent to func(*args, **kwargs)Most Dask users will never need to use the apply function. It is typically only used by people who need to inject keyword argument values into a low level Dask task graph.

Parameters

funccallable

The function you want to apply.

argstuple

A tuple containing all the positional arguments needed for func(eg: (arg_1, arg_2, arg_3))

kwargsdict, optional

A dictionary mapping the keyword arguments (eg: {"kwarg_1": value, "kwarg_2": value}

Examples

from dask.utils import apply def add(number, second_number=5): ... return number + second_number ... apply(add, (10,), {"second_number": 2}) # equivalent to add(*args, **kwargs) 12

task = apply(add, (10,), {"second_number": 2}) dsk = {'task-name': task} # adds the task to a low level Dask task graph

dask.utils.format_bytes(n: int) → str[source]

Format bytes as text

from dask.utils import format_bytes format_bytes(1) '1 B' format_bytes(1234) '1.21 kiB' format_bytes(12345678) '11.77 MiB' format_bytes(1234567890) '1.15 GiB' format_bytes(1234567890000) '1.12 TiB' format_bytes(1234567890000000) '1.10 PiB'

For all values < 2**60, the output is always <= 10 characters.

dask.utils.format_time(n: float) → str[source]

format integers as time

from dask.utils import format_time format_time(1) '1.00 s' format_time(0.001234) '1.23 ms' format_time(0.00012345) '123.45 us' format_time(123.456) '123.46 s' format_time(1234.567) '20m 34s' format_time(12345.67) '3hr 25m' format_time(123456.78) '34hr 17m' format_time(1234567.89) '14d 6hr'

dask.utils.parse_bytes(s: float | str) → int[source]

Parse byte string to numbers

from dask.utils import parse_bytes parse_bytes('100') 100 parse_bytes('100 MB') 100000000 parse_bytes('100M') 100000000 parse_bytes('5kB') 5000 parse_bytes('5.4 kB') 5400 parse_bytes('1kiB') 1024 parse_bytes('1e6') 1000000 parse_bytes('1e6 kB') 1000000000 parse_bytes('MB') 1000000 parse_bytes(123) 123 parse_bytes('5 foos') Traceback (most recent call last): ... ValueError: Could not interpret 'foos' as a byte unit

dask.utils.parse_timedelta(s: None, default: str | Literal[False] = 'seconds') → None[source]

dask.utils.parse_timedelta(s: str | float | timedelta, default: str | Literal[False] = 'seconds') → float

Parse timedelta string to number of seconds

Parameters

sstr, float, timedelta, or None

default: str or False, optional

Unit of measure if s does not specify one. Defaults to seconds. Set to False to require s to explicitly specify its own unit.

Examples

from datetime import timedelta from dask.utils import parse_timedelta parse_timedelta('3s') 3 parse_timedelta('3.5 seconds') 3.5 parse_timedelta('300ms') 0.3 parse_timedelta(timedelta(seconds=3)) # also supports timedeltas 3