Return the cumulative sum of the elements along the given axis. Refer to :func:`dask.array.cumsum` for full documentation. See Also

>>> key_split('x') 'x' >>> key_split('x-1') 'x' >>> key_split('x-1-2-3') 'x' >>> key_split(('x-2', 1)) 'x' >>> ke

Filter strings by those that match a pattern. Examples -------- >>> import dask.bag as db >>> b = db.from_sequence([

See dd.to_json docstring for more information

Shutdown the resource tracker process

Construct a Dask DataFrame from a Python Dictionary See Also -------- dask.dataframe.from_dict

Is x a runnable task? A task is a tuple with a callable first argument Examples -------- >>> inc = lambda x: x + 1 >>> istask((

Return the product of the array elements over the given axis Refer to :func:`dask.array.prod` for full documentation. See Also

Read json files into a DataFrame collection Parameters ---------- url_path : str or list Source path(s).

Substitute a specific term within the expression Note that replacing non-`Expr` terms may produce unexpected results, and is not reco

Efficiently convert to plain dict. This method is faster than dict(self).

Validate an input to axis= keywords

The keys for this expression This is used to determine the keys of the output collection when this expression is computed. R

Broadcast an array to a new shape. Parameters ---------- x : array_like The array to broadcast. shape : tuple The sha

Fuse tasks that form reductions; more advanced than ``fuse_linear`` This trades parallelism opportunities for faster scheduling by making tasks

Get a dask DataFrame/Series representing the `nth` partition. Parameters ---------- n : int The 0-indexe

Create timeseries dataframe with random data Parameters ---------- start: datetime (or datetime-like string) Start of time series

Convert a generic Mapping into a dict. Optimize use case of :class:`~dask.highlevelgraph.HighLevelGraph`. Parameters ---------- d : M

Parse byte string to numbers >>> from dask.utils import parse_bytes >>> parse_bytes('100') 100 >>> parse_bytes('100 MB') 10000000

Cartesian product between two bags.

>>> a, b, c = 'abc' >>> d = {a: [b, c], b: [c]} >>> reverse_dict(d) # doctest: +SKIP {'a': set([]), 'b': set(['a']}, 'c': set(['a',

Traverse expression tree, collect layers Subclasses generally do not want to override this method unless custom logic is required to

Depending on config setting, we might convert objects to pyarrow strings

Convert a dask DataFrame to a dask array. Parameters ---------- lengths : bool or Sequence of ints, optional How

Returns True if any of the elements evaluate to True. Refer to :func:`dask.array.any` for full documentation. See Also -----

Compute the chunk sizes for a Dask array. This is especially useful when the chunk sizes are unknown (e.g., when indexing one Dask ar

Repartition Bag across new divisions. Parameters ---------- npartitions : int, optional Number of partitions of o

Compute several dask collections at once. Parameters ---------- args : object Any number of objects. If it is a dask object, it's

Compute a graph as if it were of type cls. Allows for applying the same optimizations and default scheduler.

Multiprocessed get function appropriate for Bags Parameters ---------- dsk : dict dask graph keys : object or list De

A naive synchronous version of get_async Can be useful for debugging.

Returns whether the categories in `x` are known. Parameters ---------- x : Series or CategoricalIndex

Lower an expression completely This calls the ``lower_once`` method in a loop until nothing changes. This function does not a

Apply a function to each partition, sharing rows with adjacent partitions. This can be useful for implementing windowing functions such as

Return the maximum along a given axis. Refer to :func:`dask.array.max` for full documentation. See Also -------- das

Create a spreadsheet-style pivot table as a DataFrame. Target ``columns`` must have category dtype to infer result's ``columns``.

Repeat elements of an array. Refer to :func:`dask.array.repeat` for full documentation. See Also -------- dask.array

Store dask arrays in array-like objects, overwrite data in target This stores dask arrays into object that supports numpy-style setitem index

Do inputs come from the same parents, modulo blockwise?

Generate descriptive statistics. Dask computes percentiles (used for the ``25%``, ``50%``, and ``75%`` statistics) using an **approxi

keys are the keys from the graph that are requested by a computation. We can't fuse those together.

Return the maximum along a given axis. Refer to :func:`dask.array.max` for full documentation. See Also -------- das

Return the minimum along a given axis. Refer to :func:`dask.array.min` for full documentation. See Also -------- das

Convert into a list of ``dask.delayed`` objects, one per partition. Parameters ---------- optimize_graph : bool, optional

Get a view of the array as a new data type Parameters ---------- dtype: The dtype by which to view the array.

Convert columns of the DataFrame to category dtype. .. warning:: This method eagerly computes the categories of the chosen columns.

Create dask array from something that looks like an array. Input must have a ``.shape``, ``.ndim``, ``.dtype`` and support numpy-style slicing.

Get scheduler function There are various ways to specify the scheduler to use: 1. Passing in scheduler= parameters 2. Passing these in