Streamz high-level collection APIs are built on top of streamz.core, and bring special consideration to certain types of data:

  1. streamz.batch: supports streams of lists of Python objects like tuples or dictionaries
  2. streamz.dataframe: supports streams of Pandas/cudf dataframes or Pandas/cudf series. cudf support is in beta phase and has limited functionality as of cudf version 0.8

These high-level APIs help us handle common situations in data processing. They help us implement complex algorithms and also improve efficiency.

These APIs are built on the streamz core operations (map, accumulate, buffer, timed_window, …) which provide the building blocks to build complex pipelines but offer no help with what those functions should be. The higher-level APIs help to fill in this gap for common situations.


Stream.to_batch(**kwargs) Convert a stream of lists to a Batch
Stream.to_dataframe(example) Convert a stream of Pandas dataframes to a DataFrame

You can convert from core Stream objects to Batch, and DataFrame objects using the .to_batch and .to_dataframe methods. In each case we assume that the stream is a stream of batches (lists or tuples) or a list of Pandas dataframes.

>>> batch = stream.to_batch()
>>> sdf = stream.to_dataframe()

To convert back from a Batch or a DataFrame to a core.Stream you can access the .stream property.

>>> stream =
>>> stream =


We create a stream and connect it to a file object

file = ...  # filename or file-like object
from streamz import Stream

source = Stream.from_textfile(file)

Our file produces line-delimited JSON serialized data on which we want to call json.loads to parse into dictionaries.

To reduce overhead we first batch our records up into 100-line batches and turn this into a Batch object. We provide our Batch object an example element that it will use to help it determine metadata.

example = [{'name': 'Alice', 'x': 1, 'y': 2}]
lines = source.partition(100).to_batch(example=example)  # batches of 100 elements
records =  # convert lines to text.

We could have done the .map(json.loads) command on the original stream, but this way reduce overhead by applying this function to lists of items, rather than one item at a time.

Now we convert these batches of records into pandas dataframes and do some basic filtering and groupby-aggregations.

sdf = records.to_dataframe()
sdf = sdf[ == "Alice"]
sdf = sdf.groupby(sdf.x).y.mean()

The DataFrames satisfy a subset of the Pandas API, but now rather than operate on the data directly, they set up a pipeline to compute the data in an online fashion.

Finally we convert this back to a stream and push the results into a fixed-size deque.

from collections import deque
d = deque(maxlen=10)

See Collections API for more information.