Normalize slicing indexes 1. Replaces ellipses with many full slices 2. Adds full slices to end of index 3. Checks bounding conditions

Optimize dask for array computation 1. Cull tasks not necessary to evaluate keys 2. Perform linear fusion

Share boundaries between neighboring blocks Parameters ---------- x: da.Array A dask array axes: dict The size of th

Partition a bag along a grouper, store partitions on disk.

Plan an iterative rechunking from *old_chunks* to *new_chunks*. The plan aims to minimize the rechunk graph size. Parameters ----------

Ensure that this value remains this value in a dask graph Some values in dask graph take on special meaning. Sometimes we want to ensure that

Register a target class for a specific array-backend label

Repartition dataframe along new divisions Dask.DataFrame objects are partitioned along their index. Often when multiple dataframes interact

Remove trivial sequential alias chains Example: dsk = {'x': 1, 'y': Alias('x'), 'z': Alias('y')} resolve_aliases(dsk, {'z'}, {'

Randomly compressed rank-k thin Singular Value Decomposition. This computes the approximate singular value decomposition of a large array. T

Index array with an iterable of index Handles a single index by a single list Mimics ``np.take`` >>> from pprint import pprint >>>

Move to a new DataFrame backend Parameters ---------- backend : str, Optional The name of the new backend to move

Move to a new Array backend Parameters ---------- backend : str, Optional The name of the new backend to move to.

Convert dask Array to dask Dataframe Parameters ---------- columns: list or string list of column names if DataFr

Convert into an array of :class:`dask.delayed.Delayed` objects, one per chunk. Parameters ---------- optimize_graph : bool, o

See dd.to_orc docstring for more information

Returns the variance of the array elements, along given axis. Refer to :func:`dask.array.var` for full documentation. See Also

Visualize the results of profiling in a bokeh plot. If multiple profilers are passed in, the plots are stacked vertically. Parameters --

Are all columns that we're not grouping on numeric?

Append row-group metadata and include a helpful error message if an inconsistent schema is detected.

Merge intermediate results from ``arg_*`` functions

Transform values to be comparable against divisions This is particularly valuable to use with pandas datetimes

Determine the correct levels argument to groupby.

Helper to generate metadata for map_partitions and map_overlap output.

Get locations to search for YAML configuration files. This logic exists as a separate function for testing purposes.

Check if obj is a dask Future (TaskRef or duck-typed with __dask_future__). This supports both distributed.Future (which inherits from TaskRef) a

The graph layer added by this expression. Simple expressions that apply one task per partition can choose to only implement `Expr._ta

Add `entry_point` command to `interface`. Parameters ---------- interface : click.Command or click.Group The click interface to a

Check whether Dask should delegate to the other. This implementation follows NEP-13: https://numpy.org/neps/nep-0013-ufunc-overrides.html#beha

Returns a dict of {blocknum: slice} This function figures out where each slice should start in each block for a single dimension. If the slic

Perform the tree reduction step of a reduction. Lower level, users should use ``reduction`` or ``arg_reduction`` directly.

Common code between all 3 t-test functions.

Repeatedly apply binary function to a sequence, accumulating results. This assumes that the bag is ordered. While this is typically the case

Parallel version of pandas.DataFrame.apply This mimics the pandas version except for the following: 1. Only ``axis=1`` is supported

Return indices of the maximum values along the given axis. Refer to :func:`dask.array.argmax` for full documentation. See Also

Get the arguments for the current task

Ensure the categories in this series are known. If the categories are known, this is a no-op. If unknown, the categories are computed

Test that Series.map() behaves identically in Pandas and Dask when `func` is a Series, and that Dask's behaviour is coherent in the edge cases

Make ``children`` collection(s), optionally omitting sub-collections, dependent on ``parents`` collection(s). Two examples follow. The f

Assemble an nd-array from nested lists of blocks. Blocks in the innermost lists are concatenated along the last dimension (-1), then the