# API Reference

Dask APIs generally follow from upstream APIs:

- Arrays follows NumPy

- DataFrames follows Pandas

- Bag follows map/filter/groupby/reduce common in Spark and Python iterators

- Delayed wraps general Python code

- Futures follows concurrent.futures from the standard library for real-time computation.

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 a `dask.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) → Iterator[None]

  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:
    **annotations key-value pairs

  See also

  `get_annotations`

  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]

  Get current annotations.

  Returns:
    Dict of all current annotations

  See also

  `annotate`

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

  Compute several dask collections at once.

  Parameters:
    args object

      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 the `traverse` keyword). Non-dask arguments are passed through unchanged.

    traverse bool, 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, set `traverse=False` to avoid doing this traversal.

    scheduler string, 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_graph bool, 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.

    get `None`

      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

  Returns `True` if `x` is a dask collection.

  Parameters:
    x Any

      Object to test.

  Returns:
    result bool

      `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)

  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 method directly.

  Parameters:
    *args objects

      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.

    traverse bool, 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, set `traverse=False` to avoid doing this traversal.

    optimizations list 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)

  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
    scheduler string, 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.

    traverse bool, 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, set `traverse=False` to avoid doing this traversal.

    optimize_graph bool, 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: Literal['cytoscape', 'ipycytoscape', 'graphviz'] | None = None, **kwargs)

  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:
    args object

      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.

    filename str or None, optional

      The name of the file to write to disk. If the provided filename doesn’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’.

    traverse bool, 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, set `traverse=False` to avoid doing this traversal.

    optimize_graph bool, 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:

      - None, the default, no colors.

      - ‘order’, colors the nodes’ border based on the order they appear in the graph.

      - ‘ages’, how long the data of a node is held.

      - ‘freed’, the number of dependencies released after running a node.

      - ‘memoryincreases’, how many more outputs are held after the lifetime of a node. Large values may indicate nodes that should have run later.

      - ‘memorydecreases’, how many fewer outputs are held after the lifetime of a node. Large values may indicate nodes that should have run sooner.

      - ‘memorypressure’, the number of data held when the node is run (circle), or the data is released (rectangle).

    maxval{int, float}, optional

      Maximum value for colormap to normalize form 0 to 1.0. Default is `None` will make it the max number of values

    collapse_outputs bool, optional

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

    verbose bool, 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 `graphviz` and `ipycytoscape`, using the first one to succeed.

    **kwargs

      Additional keyword arguments to forward to the visualization engine.

  Returns:
    result IPython.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')

  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:
    npartitions int

      Number of partitions

    records_per_partition int

      Number of records in each partition

    seed int, (optional)

      Random seed

    locale str

      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)

  Create timeseries dataframe with random data

  Parameters:
    start datetime (or datetime-like string)

      Start of time series

    end datetime (or datetime-like string)

      End of time series

    dtypes dict (optional)

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

    freq string

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

    partition_freq string

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

    seed int (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: DatasetSpec, seed: int | None = None)

  Generate a random dataset according to provided spec

  Parameters:
    spec DatasetSpec

      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?j~N/-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 = <factory>, low: int | None = None, high: int | None = None, length: int | None = None, random: bool = False, method: str | None = None, args: tuple[~typing.Any, ...]=<factory>, kwargs: dict[str, ~typing.Any]=<factory>)

  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)

  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)

  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: RangeIndexSpec | DatetimeIndexSpec = <factory>, column_specs: list[ColumnSpec] = <factory>)

  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[ColumnSpec]

    List of column definitions

  index_spec: RangeIndexSpec | 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)

  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:
    func callable

      The function you want to apply.

    args tuple

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

    kwargs dict, 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

  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

  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

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

  Parse timedelta string to number of seconds

  Parameters:
    s str, 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