Contents

Changelog

Contents

Changelog

2024.3.1

This is a minor release that primarily demotes an exception to a warning if dask-expr is not installed when upgrading.

Additional changes

2024.3.0

Released on March 11, 2024

Highlights

Query planning

This release is enabling query planning by default for all users of dask.dataframe.

The query planning functionality represents a rewrite of the DataFrame using dask-expr. This is a drop-in replacement and we expect that most users will not have to adjust any of their code. Any feedback can be reported on the Dask issue tracker or on the query planning feedback issue.

If you are encountering any issues you are still able to opt-out by setting

>>> import dask
>>> dask.config.set({'dataframe.query-planning': False})

Sunset of Pandas 1.X support

The new query planning backend is requiring at least pandas 2.0. This pandas version will automatically be installed if you are installing from conda or if you are installing using dask[complete] or dask[dataframe] from pip.

The legacy DataFrame implementation is still supporting pandas 1.X if you install dask without extras.

Additional changes

2024.2.1

Released on February 23, 2024

Highlights

Allow silencing dask.DataFrame deprecation warning

The last release contained a DeprecationWarning that alerts users to an upcoming switch of dask.dafaframe to use the new backend with support for query planning (see also GH#10934).

This DeprecationWarning is triggered in import of the dask.dataframe module and the community raised concerns about this being to verbose.

It is now possible to silence this warning

# via Python
>>> dask.config.set({'dataframe.query-planning-warning': False})

# via CLI
dask config set dataframe.query-planning-warning False

See GH#10936 and GH#10925 from Miles for details.

More robust distributed scheduler for rare key collisions

Blockwise fusion optimization can cause a task key collision that is not being handled properly by the distributed scheduler (see GH#9888). Users will typically notice this by seeing one of various internal exceptions that cause a system deadlock or critical failure. While this issue could not be fixed, the scheduler now implements a mechanism that should mitigate most occurences and issues a warning if the issue is detected.

See GH#8185 from crusaderky and Florian Jetter for details.

Over the course of this, various improvements to tokenization have been implemented. See GH#10913, GH#10884, GH#10919, GH#10896 and primarily GH#10883 from crusaderky for more details.

More robust adaptive scaling on large clusters

Adaptive scaling could previously lose data during downscaling if many tasks had to be moved. This typically, but not exclusively, occured on large clusters and would manifest as a recomputation of tasks and could cause clusters to oscillate between up- and downscaling without ever finishing.

See GH#8522 from crusaderky for more details.

Additional changes

2024.2.0

Released on February 9, 2024

Highlights

Deprecate Dask DataFrame implementation

The current Dask DataFrame implementation is deprecated. In a future release, Dask DataFrame will use new implementation that contains several improvements including a logical query planning. The user-facing DataFrame API will remain unchanged.

The new implementation is already available and can be enabled by installing the dask-expr library:

$ pip install dask-expr

and turning the query planning option on:

>>> import dask
>>> dask.config.set({'dataframe.query-planning': True})
>>> import dask.dataframe as dd

API documentation for the new implementation is available at https://docs.dask.org/en/stable/dask-expr-api.html

Any feedback can be reported on the Dask issue tracker https://github.com/dask/dask/issues

See GH#10912 from Patrick Hoefler for details.

Improved tokenization

This release contains several improvements to Dask’s object tokenization logic. More objects now produce deterministic tokens, which can lead to improved performance through caching of intermediate results.

See GH#10898, GH#10904, GH#10876, GH#10874, and GH#10865 from crusaderky for details.

Additional changes

2024.1.1

Released on January 26, 2024

Highlights

Pandas 2.2 and Scipy 1.12 support

This release contains compatibility updates for the latest pandas and scipy releases.

See GH#10834, GH#10849, GH#10845, and GH#8474 from crusaderky for details.

Deprecations

Additional changes

2024.1.0

Released on January 12, 2024

Highlights

Partial rechunks within P2P

P2P rechunking now utilizes the relationships between input and output chunks. For situations that do not require all-to-all data transfer, this may significantly reduce the runtime and memory/disk footprint. It also enables task culling.

See GH#8330 from Hendrik Makait for details.

Fastparquet engine deprecated

The fastparquet Parquet engine has been deprecated. Users should migrate to the pyarrow engine by installing PyArrow and removing engine="fastparquet" in read_parquet or to_parquet calls.

See GH#10743 from crusaderky for details.

Improved serialization for arbitrary data

This release improves serialization robustness for arbitrary data. Previously there were some cases where serialization could fail for non-msgpack serializable data. In those cases we now fallback to using pickle.

See GH#8447 from Hendrik Makait for details.

Additional deprecations

Additional changes

2023.12.1

Released on December 15, 2023

Highlights

Logical Query Planning now available for Dask DataFrames

Dask DataFrames are now much more performant by using a logical query planner. This feature is currently off by default, but can be turned on with:

dask.config.set({"dataframe.query-planning": True})

You also need to have dask-expr installed:

pip install dask-expr

We’ve seen promising performance improvements so far, see this blog post and these regularly updated benchmarks for more information. A more detailed explanation of how the query optimizer works can be found in this blog post.

This feature is still under active development and the API isn’t stable yet, so breaking changes can occur. We expect to make the query optimizer the default early next year.

See GH#10634 from Patrick Hoefler for details.

Dtype inference in read_parquet

read_parquet will now infer the Arrow types pa.date32(), pa.date64() and pa.decimal() as a ArrowDtype in pandas. These dtypes are backed by the original Arrow array, and thus avoid the conversion to NumPy object. Additionally, read_parquet will no longer infer nested and binary types as strings, they will be stored in NumPy object arrays.

See GH#10698 and GH#10705 from Patrick Hoefler for details.

Scheduling improvements to reduce memory usage

This release includes a major rewrite to a core part of our scheduling logic. It includes a new approach to the topological sorting algorithm in dask.order which determines the order in which tasks are run. Improper ordering is known to be a major contributor to too large cluster memory pressure.

Updates in this release fix a couple of performance regressions that were introduced in the release 2023.10.0 (see GH#10535). Generally, computations should now be much more eager to release data if it is no longer required in memory.

See GH#10660, GH#10697 from Florian Jetter for details.

Improved P2P-based merging robustness and performance

This release contains several updates that fix a possible deadlock introduced in 2023.9.2 and improve the robustness of P2P-based merging when the cluster is dynamically scaling up.

See GH#8415, GH#8416, and GH#8414 from Hendrik Makait for details.

Removed disabling pickle option

The distributed.scheduler.pickle configuration option is no longer supported. As of the 2023.4.0 release, pickle is used to transmit task graphs, so can no longer be disabled. We now raise an informative error when distributed.scheduler.pickle is set to False.

See GH#8401 from Florian Jetter for details.

Additional changes

2023.12.0

Released on December 1, 2023

Highlights

PipInstall restart and environment variables

The distributed.PipInstall plugin now has more robust restart logic and also supports environment variables.

Below shows how users can use the distributed.PipInstall plugin and a TOKEN environment variable to securely install a package from a private repository:

from dask.distributed import PipInstall
plugin = PipInstall(packages=["private_package@git+https://${TOKEN}@github.com/dask/private_package.git])
client.register_plugin(plugin)

See GH#8374, GH#8357, and GH#8343 from Hendrik Makait for details.

Bokeh 3.3.0 compatibility

This release contains compatibility updates for using bokeh>=3.3.0 with proxied Dask dashboards. Previously the contents of dashboard plots wouldn’t be displayed.

See GH#8347 and GH#8381 from Jacob Tomlinson for details.

Additional changes

2023.11.0

Released on November 10, 2023

Highlights

Zero-copy P2P Array Rechunking

Users should see significant performance improvements when using in-memory P2P array rechunking. This is due to no longer copying underlying data buffers.

Below shows a simple example where we compare performance of different rechunking methods.

shape = (30_000, 6_000, 150) # 201.17 GiB
input_chunks = (60, -1, -1) # 411.99 MiB
output_chunks = (-1, 6, -1) # 205.99 MiB

arr = da.random.random(size, chunks=input_chunks)
with dask.config.set({
    "array.rechunk.method": "p2p",
    "distributed.p2p.disk": True,
}):
    (
      da.random.random(size, chunks=input_chunks)
      .rechunk(output_chunks)
      .sum()
      .compute()
    )
A comparison of rechunking performance between the different methods tasks, p2p with disk and p2p without disk on different cluster sizes. The graph shows that p2p without disk is up to 60% faster than the default tasks based approach.

See GH#8282, GH#8318, GH#8321 from crusaderky and (GH#8322) from Hendrik Makait for details.

Deprecating PyArrow <14.0.1

pyarrow<14.0.1 usage is deprecated starting in this release. It’s recommended for all users to upgrade their version of pyarrow or install pyarrow-hotfix. See this CVE for full details.

See GH#10622 from Florian Jetter for details.

Improved PyArrow filesystem for Parquet

Using filesystem="arrow" when reading Parquet datasets now properly inferrs the correct cloud region when accessing remote, cloud-hosted data.

See GH#10590 from Richard (Rick) Zamora for details.

Improve Type Reconciliation in P2P Shuffling

See GH#8332 from Hendrik Makait for details.

Additional changes

2023.10.1

Released on October 27, 2023

Highlights

Python 3.12

This release adds official support for Python 3.12.

See GH#10544 and GH#8223 from Thomas Grainger for details.

Additional changes

2023.10.0

Released on October 13, 2023

Highlights

Reduced memory pressure for multi array reductions

This release contains major updates to Dask’s task graph scheduling logic. The updates here significantly reduce memory pressure on array reductions. We anticipate this will have a strong impact on the array computing community.

See GH#10535 from Florian Jetter for details.

Improved P2P shuffling robustness

There are several updates (listed below) that make P2P shuffling much more robust and less likely to fail.

See GH#8262, GH#8264, GH#8242, GH#8244, and GH#8235 from Hendrik Makait and GH#8124 from Charles Blackmon-Luca for details.

Reduced scheduler CPU load for large graphs

Users should see reduced CPU load on their scheduler when computing large task graphs.

See GH#8238 and GH#10547 from Florian Jetter and GH#8240 from crusaderky for details.

Additional changes

2023.9.3

Released on September 29, 2023

Highlights

Restore previous configuration override behavior

The 2023.9.2 release introduced an unintentional breaking change in how configuration options are overriden in dask.config.get with the override_with= keyword (see GH#10519). This release restores the previous behavior.

See GH#10521 from crusaderky for details.

Complex dtypes in Dask Array reductions

This release includes improved support for using common reductions in Dask Array (e.g. var, std, moment) with complex dtypes.

See GH#10009 from wkrasnicki for details.

Additional changes

2023.9.2

Released on September 15, 2023

Highlights

P2P shuffling now raises when outdated PyArrow is installed

Previously the default shuffling method would silently fallback from P2P to task-based shuffling if an older version of pyarrow was installed. Now we raise an informative error with the minimum required pyarrow version for P2P instead of silently falling back.

See GH#10496 from Hendrik Makait for details.

Deprecation cycle for admin.traceback.shorten

The 2023.9.0 release modified the admin.traceback.shorten configuration option without introducing a deprecation cycle. This resulted in failures to create Dask clusters in some cases. This release introduces a deprecation cycle for this configuration change.

See GH#10509 from crusaderky for details.

Additional changes

2023.9.1

Released on September 6, 2023

Note

This is a hotfix release that fixes a P2P shuffling bug introduced in the 2023.9.0 release (see GH#10493).

Enhancements

Bug Fixes

Maintenance

2023.9.0

Released on September 1, 2023

Bug Fixes

Documentation

Maintenance

2023.8.1

Released on August 18, 2023

Enhancements

Bug Fixes

  • Fix ValueError when running to_csv in append mode with single_file as True (GH#10441) Ben

Maintenance

2023.8.0

Released on August 4, 2023

Enhancements

Documentation

Maintenance

2023.7.1

Released on July 20, 2023

Note

This release updates Dask DataFrame to automatically convert text data using object data types to string[pyarrow] if pandas>=2 and pyarrow>=12 are installed.

This should result in significantly reduced memory consumption and increased computation performance in many workflows that deal with text data.

You can disable this change by setting the dataframe.convert-string configuration value to False with

dask.config.set({"dataframe.convert-string": False})

Enhancements

Bug Fixes

2023.7.0

Released on July 7, 2023

Enhancements

Bug Fixes

Documentation

  • Add clarification about output shape and reshaping in rechunk documentation (GH#10377) Swayam Patil

Maintenance

2023.6.1

Released on June 26, 2023

Enhancements

Bug Fixes

Deprecations

Maintenance

2023.6.0

Released on June 9, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.5.1

Released on May 26, 2023

Note

This release drops support for Python 3.8. As of this release Dask supports Python 3.9, 3.10, and 3.11. See this community issue for more details.

Enhancements

Bug Fixes

Documentation

Maintenance

2023.5.0

Released on May 12, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.4.1

Released on April 28, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.4.0

Released on April 14, 2023

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2023.3.2

Released on March 24, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.3.1

Released on March 10, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.3.0

Released on March 1, 2023

Bug Fixes

Documentation

Maintenance

2023.2.1

Released on February 24, 2023

Note

This release changes the default DataFrame shuffle algorithm to p2p to improve stability and performance. Learn more here and please provide any feedback on this discussion.

If you encounter issues with this new algorithm, please see the documentation for more information, and how to switch back to the old mode.

Enhancements

Bug Fixes

Documentation

Maintenance

2023.2.0

Released on February 10, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.1.1

Released on January 27, 2023

Enhancements

Bug Fixes

Documentation

Maintenance

2023.1.0

Released on January 13, 2023

Enhancements

Documentation

Maintenance

2022.12.1

Released on December 16, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.12.0

Released on December 2, 2022

Enhancements

Bug Fixes

Maintenance

2022.11.1

Released on November 18, 2022

Enhancements

Maintenance

2022.11.0

Released on November 15, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.10.2

Released on October 31, 2022

This was a hotfix and has no changes in this repository. The necessary fix was in dask/distributed, but we decided to bump this version number for consistency.

2022.10.1

Released on October 28, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.10.0

Released on October 14, 2022

New Features

Enhancements

Bug Fixes

Documentation

Maintenance

2022.9.2

Released on September 30, 2022

Enhancements

Documentation

Maintenance

2022.9.1

Released on September 16, 2022

New Features

Enhancements

Bug Fixes

Deprecations

  • Allow split_out to be None, which then defaults to 1 in groupby().aggregate() (GH#9491) Ian Rose

Documentation

Maintenance

2022.9.0

Released on September 2, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.8.1

Released on August 19, 2022

New Features

Enhancements

Bug Fixes

Documentation

Maintenance

2022.8.0

Released on August 5, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.7.1

Released on July 22, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.7.0

Released on July 8, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.6.1

Released on June 24, 2022

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.6.0

Released on June 10, 2022

Enhancements

Bug Fixes

Documentation

Maintenance

2022.05.2

Released on May 26, 2022

Enhancements

Documentation

Maintenance

2022.05.1

Released on May 24, 2022

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.05.0

Released on May 2, 2022

Highlights

This is a bugfix release for this issue.

Documentation

2022.04.2

Released on April 29, 2022

Highlights

This release includes several deprecations/breaking API changes to dask.dataframe.read_parquet and dask.dataframe.to_parquet:

  • to_parquet no longer writes _metadata files by default. If you want to write a _metadata file, you can pass in write_metadata_file=True.

  • read_parquet now defaults to split_row_groups=False, which results in one Dask dataframe partition per parquet file when reading in a parquet dataset. If you’re working with large parquet files you may need to set split_row_groups=True to reduce your partition size.

  • read_parquet no longer calculates divisions by default. If you require read_parquet to return dataframes with known divisions, please set calculate_divisions=True.

  • read_parquet has deprecated the gather_statistics keyword argument. Please use the calculate_divisions keyword argument instead.

  • read_parquet has deprecated the require_extensions keyword argument. Please use the parquet_file_extension keyword argument instead.

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.04.1

Released on April 15, 2022

New Features

  • Add missing NumPy ufuncs: abs, left_shift, right_shift, positive. (GH#8920) Tom White

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.04.0

Released on April 1, 2022

Note

This is the first release with support for Python 3.10

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.03.0

Released on March 18, 2022

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.02.1

Released on February 25, 2022

New Features

  • Add aggregate functions first and last to dask.dataframe.pivot_table (GH#8649) Knut Nordanger

  • Add std() support for datetime64 dtype for pandas-like objects (GH#8523) Ben Glossner

  • Add materialized task counts to HighLevelGraph and Layer html reprs (GH#8589) kori73

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.02.0

Released on February 11, 2022

Note

This is the last release with support for Python 3.7

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.01.1

Released on January 28, 2022

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2022.01.0

Released on January 14, 2022

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2021.12.0

Released on December 10, 2021

New Features

Enhancements

Bug Fixes

Deprecations

Documentation

Maintenance

2021.11.2

Released on November 19, 2021

2021.11.1

Released on November 8, 2021

Patch release to update distributed dependency to version 2021.11.1.

2021.11.0

Released on November 5, 2021

2021.10.0

Released on October 22, 2021

2021.09.1

Released on September 21, 2021

2021.09.0

Released on September 3, 2021

2021.08.1

Released on August 20, 2021

2021.08.0

Released on August 13, 2021

2021.07.2

Released on July 30, 2021

Note

This is the last release with support for NumPy 1.17 and pandas 0.25. Beginning with the next release, NumPy 1.18 and pandas 1.0 will be the minimum supported versions.

2021.07.1

Released on July 23, 2021

2021.07.0

Released on July 9, 2021

2021.06.2

Released on June 22, 2021

2021.06.1

Released on June 18, 2021

2021.06.0

Released on June 4, 2021

2021.05.1

Released on May 28, 2021

2021.05.0

Released on May 14, 2021

2021.04.1

Released on April 23, 2021

2021.04.0

Released on April 2, 2021

2021.03.1

Released on March 26, 2021

2021.03.0

Released on March 5, 2021

Note

This is the first release with support for Python 3.9 and the last release with support for Python 3.6

2021.02.0

Released on February 5, 2021

2021.01.1

Released on January 22, 2021

2021.01.0

Released on January 15, 2021

2020.12.0

Released on December 10, 2020

Highlights

  • Switched to CalVer for versioning scheme.

  • Introduced new APIs for HighLevelGraph to enable sending high-level representations of task graphs to the distributed scheduler.

  • Introduced new HighLevelGraph layer objects including BasicLayer, Blockwise, BlockwiseIO, ShuffleLayer, and more.

  • Added support for applying custom Layer-level annotations like priority, retries, etc. with the dask.annotations context manager.

  • Updated minimum supported version of pandas to 0.25.0 and NumPy to 1.15.1.

  • Support for the pyarrow.dataset API to read_parquet.

  • Several fixes to Dask Array’s SVD.

All changes

2.30.0 / 2020-10-06

Array

2.29.0 / 2020-10-02

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2.28.0 / 2020-09-25

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2.27.0 / 2020-09-18

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2.26.0 / 2020-09-11

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2.25.0 / 2020-08-28

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DataFrame

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2.24.0 / 2020-08-22

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2.23.0 / 2020-08-14

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2.22.0 / 2020-07-31

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2.21.0 / 2020-07-17

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2.20.0 / 2020-07-02

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2.19.0 / 2020-06-19

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2.18.1 / 2020-06-09

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2.18.0 / 2020-06-05

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2.17.2 / 2020-05-28

Core

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2.17.1 / 2020-05-28

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2.17.0 / 2020-05-26

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2.16.0 / 2020-05-08

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2.15.0 / 2020-04-24

Array

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2.14.0 / 2020-04-03

Array

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2.13.0 / 2020-03-25

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2.12.0 / 2020-03-06

Array

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DataFrame

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2.11.0 / 2020-02-19

Array

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2.10.1 / 2020-01-30

2.10.0 / 2020-01-28

2.9.2 / 2020-01-16

Array

Core

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2.9.1 / 2019-12-27

Array

Core

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2.9.0 / 2019-12-06

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2.8.1 / 2019-11-22

Array

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2.8.0 / 2019-11-14

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2.7.0 / 2019-11-08

This release drops support for Python 3.5

Array

Core

DataFrame

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2.6.0 / 2019-10-15

Core

DataFrame

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2.5.2 / 2019-10-04

Array

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2.5.0 / 2019-09-27

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2.4.0 / 2019-09-13

Array

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2.3.0 / 2019-08-16

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2.2.0 / 2019-08-01

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2.1.0 / 2019-07-08

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2.0.0 / 2019-06-25

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1.2.2 / 2019-05-08

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Bag

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1.2.1 / 2019-04-29

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1.2.0 / 2019-04-12

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1.1.5 / 2019-03-29

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1.1.4 / 2019-03-08

Array

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1.1.3 / 2019-03-01

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1.1.2 / 2019-02-25

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1.1.1 / 2019-01-31

Array

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Delayed

Documentation

Core

  • Work around psutil 5.5.0 not allowing pickling Process objects Janne Vuorela

1.1.0 / 2019-01-18

Array

DataFrame

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Core

1.0.0 / 2018-11-28

Array

DataFrame

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Core

0.20.2 / 2018-11-15

Array

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0.20.1 / 2018-11-09

Array

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0.20.0 / 2018-10-26

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0.19.4 / 2018-10-09

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0.19.3 / 2018-10-05

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0.19.2 / 2018-09-17

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0.19.1 / 2018-09-06

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0.19.0 / 2018-08-29

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0.18.2 / 2018-07-23

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Delayed

Core

0.18.1 / 2018-06-22

Array

DataFrame

Core

0.18.0 / 2018-06-14

Array

Dataframe

Bag

Core

0.17.5 / 2018-05-16

Array

DataFrame

0.17.4 / 2018-05-03

Dataframe

0.17.3 / 2018-05-02

Array

DataFrame

Core

  • Support traversing collections in persist, visualize, and optimize (GH#3410) Jim Crist

  • Add schedule= keyword to compute and persist. This replaces common use of the get= keyword (GH#3448) Matthew Rocklin

0.17.2 / 2018-03-21

Array

DataFrame

Bag

Core

0.17.1 / 2018-02-22

Array

DataFrame

Core

0.17.0 / 2018-02-09

Array

DataFrame

Bag

  • Document bag.map_paritions function may receive either a list or generator. (GH#3150) Nir

Core

0.16.1 / 2018-01-09

Array

DataFrame

Core

0.16.0 / 2017-11-17

This is a major release. It includes breaking changes, new protocols, and a large number of bug fixes.

Array

DataFrame

Core

0.15.4 / 2017-10-06

Array

  • da.random.choice now works with array arguments (GH#2781)

  • Support indexing in arrays with np.int (fixes regression) (GH#2719)

  • Handle zero dimension with rechunking (GH#2747)

  • Support -1 as an alias for “size of the dimension” in chunks (GH#2749)

  • Call mkdir in array.to_npy_stack (GH#2709)

DataFrame

  • Added the .str accessor to Categoricals with string categories (GH#2743)

  • Support int96 (spark) datetimes in parquet writer (GH#2711)

  • Pass on file scheme to fastparquet (GH#2714)

  • Support Pandas 0.21 (GH#2737)

Bag

  • Add tree reduction support for foldby (GH#2710)

Core

  • Drop s3fs from pip install dask[complete] (GH#2750)

0.15.3 / 2017-09-24

Array

  • Add masked arrays (GH#2301)

  • Add *_like array creation functions (GH#2640)

  • Indexing with unsigned integer array (GH#2647)

  • Improved slicing with boolean arrays of different dimensions (GH#2658)

  • Support literals in top and atop (GH#2661)

  • Optional axis argument in cumulative functions (GH#2664)

  • Improve tests on scalars with assert_eq (GH#2681)

  • Fix norm keepdims (GH#2683)

  • Add ptp (GH#2691)

  • Add apply_along_axis (GH#2690) and apply_over_axes (GH#2702)

DataFrame

  • Added Series.str[index] (GH#2634)

  • Allow the groupby by param to handle columns and index levels (GH#2636)

  • DataFrame.to_csv and Bag.to_textfiles now return the filenames to

    which they have written (GH#2655)

  • Fix combination of partition_on and append in to_parquet (GH#2645)

  • Fix for parquet file schemes (GH#2667)

  • Repartition works with mixed categoricals (GH#2676)

Core

  • python setup.py test now runs tests (GH#2641)

  • Added new cheatsheet (GH#2649)

  • Remove resize tool in Bokeh plots (GH#2688)

0.15.2 / 2017-08-25

Array

  • Remove spurious keys from map_overlap graph (GH#2520)

  • where works with non-bool condition and scalar values (GH#2543) (GH#2549)

  • Improve compress (GH#2541) (GH#2545) (GH#2555)

  • Add argwhere, _nonzero, and where(cond) (GH#2539)

  • Generalize vindex in dask.array to handle multi-dimensional indices (GH#2573)

  • Add choose method (GH#2584)

  • Split code into reorganized files (GH#2595)

  • Add linalg.norm (GH#2597)

  • Add diff, ediff1d (GH#2607), (GH#2609)

  • Improve dtype inference and reflection (GH#2571)

Bag

  • Remove deprecated Bag behaviors (GH#2525)

DataFrame

Core

  • Remove bare except: blocks everywhere (GH#2590)

0.15.1 / 2017-07-08

  • Add storage_options to to_textfiles and to_csv (GH#2466)

  • Rechunk and simplify rfftfreq (GH#2473), (GH#2475)

  • Better support ndarray subclasses (GH#2486)

  • Import star in dask.distributed (GH#2503)

  • Threadsafe cache handling with tokenization (GH#2511)

0.15.0 / 2017-06-09

Array

Bag

  • Fix bug where reductions on bags with no partitions would fail (GH#2324)

  • Add broadcasting and variadic db.map top-level function. Also remove auto-expansion of tuples as map arguments (GH#2339)

  • Rename Bag.concat to Bag.flatten (GH#2402)

DataFrame

Core

  • Move dask.async module to dask.local (GH#2318)

  • Support callbacks with nested scheduler calls (GH#2397)

  • Support pathlib.Path objects as uris (GH#2310)

0.14.3 / 2017-05-05

DataFrame

  • Pandas 0.20.0 support

0.14.2 / 2017-05-03

Array

  • Add da.indices (GH#2268), da.tile (GH#2153), da.roll (GH#2135)

  • Simultaneously support drop_axis and new_axis in da.map_blocks (GH#2264)

  • Rechunk and concatenate work with unknown chunksizes (GH#2235) and (GH#2251)

  • Support non-numpy container arrays, notably sparse arrays (GH#2234)

  • Tensordot contracts over multiple axes (GH#2186)

  • Allow delayed targets in da.store (GH#2181)

  • Support interactions against lists and tuples (GH#2148)

  • Constructor plugins for debugging (GH#2142)

  • Multi-dimensional FFTs (single chunk) (GH#2116)

Bag

  • to_dataframe enforces consistent types (GH#2199)

DataFrame

  • Set_index always fully sorts the index (GH#2290)

  • Support compatibility with pandas 0.20.0 (GH#2249), (GH#2248), and (GH#2246)

  • Support Arrow Parquet reader (GH#2223)

  • Time-based rolling windows (GH#2198)

  • Repartition can now create more partitions, not just less (GH#2168)

Core

  • Always use absolute paths when on POSIX file system (GH#2263)

  • Support user provided graph optimizations (GH#2219)

  • Refactor path handling (GH#2207)

  • Improve fusion performance (GH#2129), (GH#2131), and (GH#2112)

0.14.1 / 2017-03-22

Array

  • Micro-optimize optimizations (GH#2058)

  • Change slicing optimizations to avoid fusing raw numpy arrays (GH#2075) (GH#2080)

  • Dask.array operations now work on numpy arrays (GH#2079)

  • Reshape now works in a much broader set of cases (GH#2089)

  • Support deepcopy python protocol (GH#2090)

  • Allow user-provided FFT implementations in da.fft (GH#2093)

DataFrame

  • Fix to_parquet with empty partitions (GH#2020)

  • Optional npartitions='auto' mode in set_index (GH#2025)

  • Optimize shuffle performance (GH#2032)

  • Support efficient repartitioning along time windows like repartition(freq='12h') (GH#2059)

  • Improve speed of categorize (GH#2010)

  • Support single-row dataframe arithmetic (GH#2085)

  • Automatically avoid shuffle when setting index with a sorted column (GH#2091)

  • Improve handling of integer-na handling in read_csv (GH#2098)

Delayed

  • Repeated attribute access on delayed objects uses the same key (GH#2084)

Core

  • Improve naming of nodes in dot visuals to avoid generic apply (GH#2070)

  • Ensure that worker processes have different random seeds (GH#2094)

0.14.0 / 2017-02-24

Array

Bag

  • Repartition can now increase number of partitions (GH#1934)

  • Fix bugs in some reductions with empty partitions (GH#1939), (GH#1950), (GH#1953)

DataFrame

  • Support non-uniform categoricals (GH#1877), (GH#1930)

  • Groupby cumulative reductions (GH#1909)

  • DataFrame.loc indexing now supports lists (GH#1913)

  • Improve multi-level groupbys (GH#1914)

  • Improved HTML and string repr for DataFrames (GH#1637)

  • Parquet append (GH#1940)

  • Add dd.demo.daily_stock function for teaching (GH#1992)

Delayed

  • Add traverse= keyword to delayed to optionally avoid traversing nested data structures (GH#1899)

  • Support Futures in from_delayed functions (GH#1961)

  • Improve serialization of decorated delayed functions (GH#1969)

Core

  • Improve windows path parsing in corner cases (GH#1910)

  • Rename tasks when fusing (GH#1919)

  • Add top level persist function (GH#1927)

  • Propagate errors= keyword in byte handling (GH#1954)

  • Dask.compute traverses Python collections (GH#1975)

  • Structural sharing between graphs in dask.array and dask.delayed (GH#1985)

0.13.0 / 2017-01-02

Array

  • Mandatory dtypes on dask.array. All operations maintain dtype information and UDF functions like map_blocks now require a dtype= keyword if it can not be inferred. (GH#1755)

  • Support arrays without known shapes, such as arises when slicing arrays with arrays or converting dataframes to arrays (GH#1838)

  • Support mutation by setting one array with another (GH#1840)

  • Tree reductions for covariance and correlations. (GH#1758)

  • Add SerializableLock for better use with distributed scheduling (GH#1766)

  • Improved atop support (GH#1800)

  • Rechunk optimization (GH#1737), (GH#1827)

Bag

  • Avoid wrong results when recomputing the same groupby twice (GH#1867)

DataFrame

Delayed

  • Changed behaviour for delayed(nout=0) and delayed(nout=1): delayed(nout=1) does not default to out=None anymore, and delayed(nout=0) is also enabled. I.e. functions with return tuples of length 1 or 0 can be handled correctly. This is especially handy, if functions with a variable amount of outputs are wrapped by delayed. E.g. a trivial example: delayed(lambda *args: args, nout=len(vals))(*vals)

Core

0.12.0 / 2016-11-03

DataFrame

  • Return a series when functions given to dataframe.map_partitions return scalars (GH#1515)

  • Fix type size inference for series (GH#1513)

  • dataframe.DataFrame.categorize no longer includes missing values in the categories. This is for compatibility with a pandas change (GH#1565)

  • Fix head parser error in dataframe.read_csv when some lines have quotes (GH#1495)

  • Add dataframe.reduction and series.reduction methods to apply generic row-wise reduction to dataframes and series (GH#1483)

  • Add dataframe.select_dtypes, which mirrors the pandas method (GH#1556)

  • dataframe.read_hdf now supports reading Series (GH#1564)

  • Support Pandas 0.19.0 (GH#1540)

  • Implement select_dtypes (GH#1556)

  • String accessor works with indexes (GH#1561)

  • Add pipe method to dask.dataframe (GH#1567)

  • Add indicator keyword to merge (GH#1575)

  • Support Series in read_hdf (GH#1575)

  • Support Categories with missing values (GH#1578)

  • Support inplace operators like df.x += 1 (GH#1585)

  • Str accessor passes through args and kwargs (GH#1621)

  • Improved groupby support for single-machine multiprocessing scheduler (GH#1625)

  • Tree reductions (GH#1663)

  • Pivot tables (GH#1665)

  • Add clip (GH#1667), align (GH#1668), combine_first (GH#1725), and any/all (GH#1724)

  • Improved handling of divisions on dask-pandas merges (GH#1666)

  • Add groupby.aggregate method (GH#1678)

  • Add dd.read_table function (GH#1682)

  • Improve support for multi-level columns (GH#1697) (GH#1712)

  • Support 2d indexing in loc (GH#1726)

  • Extend resample to include DataFrames (GH#1741)

  • Support dask.array ufuncs on dask.dataframe objects (GH#1669)

Array

  • Add information about how dask.array chunks argument work (GH#1504)

  • Fix field access with non-scalar fields in dask.array (GH#1484)

  • Add concatenate= keyword to atop to concatenate chunks of contracted dimensions

  • Optimized slicing performance (GH#1539) (GH#1731)

  • Extend atop with a concatenate= (GH#1609) new_axes= (GH#1612) and adjust_chunks= (GH#1716) keywords

  • Add clip (GH#1610) swapaxes (GH#1611) round (GH#1708) repeat

  • Automatically align chunks in atop-backed operations (GH#1644)

  • Cull dask.arrays on slicing (GH#1709)

Bag

  • Fix issue with callables in bag.from_sequence being interpreted as tasks (GH#1491)

  • Avoid non-lazy memory use in reductions (GH#1747)

Administration

  • Added changelog (GH#1526)

  • Create new threadpool when operating from thread (GH#1487)

  • Unify example documentation pages into one (GH#1520)

  • Add versioneer for git-commit based versions (GH#1569)

  • Pass through node_attr and edge_attr keywords in dot visualization (GH#1614)

  • Add continuous testing for Windows with Appveyor (GH#1648)

  • Remove use of multiprocessing.Manager (GH#1653)

  • Add global optimizations keyword to compute (GH#1675)

  • Micro-optimize get_dependencies (GH#1722)

0.11.0 / 2016-08-24

Major Points

DataFrames now enforce knowing full metadata (columns, dtypes) everywhere. Previously we would operate in an ambiguous state when functions lost dtype information (such as apply). Now all dataframes always know their dtypes and raise errors asking for information if they are unable to infer (which they usually can). Some internal attributes like _pd and _pd_nonempty have been moved.

The internals of the distributed scheduler have been refactored to transition tasks between explicit states. This improves resilience, reasoning about scheduling, plugin operation, and logging. It also makes the scheduler code easier to understand for newcomers.

Breaking Changes

  • The distributed.s3 and distributed.hdfs namespaces are gone. Use protocols in normal methods like read_text('s3://...' instead.

  • Dask.array.reshape now errs in some cases where previously it would have create a very large number of tasks

0.10.2 / 2016-07-27

  • More Dataframe shuffles now work in distributed settings, ranging from setting-index to hash joins, to sorted joins and groupbys.

  • Dask passes the full test suite when run when under in Python’s optimized-OO mode.

  • On-disk shuffles were found to produce wrong results in some highly-concurrent situations, especially on Windows. This has been resolved by a fix to the partd library.

  • Fixed a growth of open file descriptors that occurred under large data communications

  • Support ports in the --bokeh-whitelist option ot dask-scheduler to better routing of web interface messages behind non-trivial network settings

  • Some improvements to resilience to worker failure (though other known failures persist)

  • You can now start an IPython kernel on any worker for improved debugging and analysis

  • Improvements to dask.dataframe.read_hdf, especially when reading from multiple files and docs

0.10.0 / 2016-06-13

Major Changes

  • This version drops support for Python 2.6

  • Conda packages are built and served from conda-forge

  • The dask.distributed executables have been renamed from dfoo to dask-foo. For example dscheduler is renamed to dask-scheduler

  • Both Bag and DataFrame include a preliminary distributed shuffle.

Bag

  • Add task-based shuffle for distributed groupbys

  • Add accumulate for cumulative reductions

DataFrame

  • Add a task-based shuffle suitable for distributed joins, groupby-applys, and set_index operations. The single-machine shuffle remains untouched (and much more efficient.)

  • Add support for new Pandas rolling API with improved communication performance on distributed systems.

  • Add groupby.std/var

  • Pass through S3/HDFS storage options in read_csv

  • Improve categorical partitioning

  • Add eval, info, isnull, notnull for dataframes

Distributed

  • Rename executables like dscheduler to dask-scheduler

  • Improve scheduler performance in the many-fast-tasks case (important for shuffling)

  • Improve work stealing to be aware of expected function run-times and data sizes. The drastically increases the breadth of algorithms that can be efficiently run on the distributed scheduler without significant user expertise.

  • Support maximum buffer sizes in streaming queues

  • Improve Windows support when using the Bokeh diagnostic web interface

  • Support compression of very-large-bytestrings in protocol

  • Support clean cancellation of submitted futures in Joblib interface

Other

  • All dask-related projects (dask, distributed, s3fs, hdfs, partd) are now building conda packages on conda-forge.

  • Change credential handling in s3fs to only pass around delegated credentials if explicitly given secret/key. The default now is to rely on managed environments. This can be changed back by explicitly providing a keyword argument. Anonymous mode must be explicitly declared if desired.

0.9.0 / 2016-05-11

API Changes

  • dask.do and dask.value have been renamed to dask.delayed

  • dask.bag.from_filenames has been renamed to dask.bag.read_text

  • All S3/HDFS data ingest functions like db.from_s3 or distributed.s3.read_csv have been moved into the plain read_text, read_csv functions, which now support protocols, like dd.read_csv('s3://bucket/keys*.csv')

Array

  • Add support for scipy.LinearOperator

  • Improve optional locking to on-disk data structures

  • Change rechunk to expose the intermediate chunks

Bag

  • Rename from_filenames to read_text

  • Remove from_s3 in favor of read_text('s3://...')

DataFrame

  • Fixed numerical stability issue for correlation and covariance

  • Allow no-hash from_pandas for speedy round-trips to and from-pandas objects

  • Generally reengineered read_csv to be more in line with Pandas behavior

  • Support fast set_index operations for sorted columns

Delayed

  • Rename do/value to delayed

  • Rename to/from_imperative to to/from_delayed

Distributed

  • Move s3 and hdfs functionality into the dask repository

  • Adaptively oversubscribe workers for very fast tasks

  • Improve PyPy support

  • Improve work stealing for unbalanced workers

  • Scatter data efficiently with tree-scatters

Other

  • Add lzma/xz compression support

  • Raise a warning when trying to split unsplittable compression types, like gzip or bz2

  • Improve hashing for single-machine shuffle operations

  • Add new callback method for start state

  • General performance tuning

0.8.1 / 2016-03-11

Array

  • Bugfix for range slicing that could periodically lead to incorrect results.

  • Improved support and resiliency of arg reductions (argmin, argmax, etc.)

Bag

  • Add zip function

DataFrame

  • Add corr and cov functions

  • Add melt function

  • Bugfixes for io to bcolz and hdf5

0.8.0 / 2016-02-20

Array

  • Changed default array reduction split from 32 to 4

  • Linear algebra, tril, triu, LU, inv, cholesky, solve, solve_triangular, eye, lstsq, diag, corrcoef.

Bag

  • Add tree reductions

  • Add range function

  • drop from_hdfs function (better functionality now exists in hdfs3 and distributed projects)

DataFrame

  • Refactor dask.dataframe to include a full empty pandas dataframe as metadata. Drop the .columns attribute on Series

  • Add Series categorical accessor, series.nunique, drop the .columns attribute for series.

  • read_csv fixes (multi-column parse_dates, integer column names, etc. )

  • Internal changes to improve graph serialization

Other

  • Documentation updates

  • Add from_imperative and to_imperative functions for all collections

  • Aesthetic changes to profiler plots

  • Moved the dask project to a new dask organization

0.7.6 / 2016-01-05

Array

  • Improve thread safety

  • Tree reductions

  • Add view, compress, hstack, dstack, vstack methods

  • map_blocks can now remove and add dimensions

DataFrame

  • Improve thread safety

  • Extend sampling to include replacement options

Imperative

  • Removed optimization passes that fused results.

Core

  • Removed dask.distributed

  • Improved performance of blocked file reading

  • Serialization improvements

  • Test Python 3.5

0.7.4 / 2015-10-23

This was mostly a bugfix release. Some notable changes:

  • Fix minor bugs associated with the release of numpy 1.10 and pandas 0.17

  • Fixed a bug with random number generation that would cause repeated blocks due to the birthday paradox

  • Use locks in dask.dataframe.read_hdf by default to avoid concurrency issues

  • Change dask.get to point to dask.async.get_sync by default

  • Allow visualization functions to accept general graphviz graph options like rankdir=’LR’

  • Add reshape and ravel to dask.array

  • Support the creation of dask.arrays from dask.imperative objects

Deprecation

This release also includes a deprecation warning for dask.distributed, which will be removed in the next version.

Future development in distributed computing for dask is happening here: https://distributed.dask.org . General feedback on that project is most welcome from this community.

0.7.3 / 2015-09-25

Diagnostics

  • A utility for profiling memory and cpu usage has been added to the dask.diagnostics module.

DataFrame

This release improves coverage of the pandas API. Among other things it includes nunique, nlargest, quantile. Fixes encoding issues with reading non-ascii csv files. Performance improvements and bug fixes with resample. More flexible read_hdf with globbing. And many more. Various bug fixes in dask.imperative and dask.bag.

0.7.0 / 2015-08-15

DataFrame

This release includes significant bugfixes and alignment with the Pandas API. This has resulted both from use and from recent involvement by Pandas core developers.

  • New operations: query, rolling operations, drop

  • Improved operations: quantiles, arithmetic on full dataframes, dropna, constructor logic, merge/join, elemwise operations, groupby aggregations

Bag

  • Fixed a bug in fold where with a null default argument

Array

  • New operations: da.fft module, da.image.imread

Infrastructure

  • The array and dataframe collections create graphs with deterministic keys. These tend to be longer (hash strings) but should be consistent between computations. This will be useful for caching in the future.

  • All collections (Array, Bag, DataFrame) inherit from common subclass

0.6.1 / 2015-07-23

Distributed

  • Improved (though not yet sufficient) resiliency for dask.distributed when workers die

DataFrame

  • Improved writing to various formats, including to_hdf, to_castra, and to_csv

  • Improved creation of dask DataFrames from dask Arrays and Bags

  • Improved support for categoricals and various other methods

Array

  • Various bug fixes

  • Histogram function

Scheduling

  • Added tie-breaking ordering of tasks within parallel workloads to better handle and clear intermediate results

Other

  • Added the dask.do function for explicit construction of graphs with normal python code

  • Traded pydot for graphviz library for graph printing to support Python3

  • There is also a gitter chat room and a stackoverflow tag