When handling large volumes of streaming tabular data it is often more efficient to pass around larger Pandas dataframes with many rows each rather than pass around individual Python tuples or dicts. Handling and computing on data with Pandas can be much faster than operating on individual Python objects.

So one could imagine building streaming dataframe pipelines using the .map and .accumulate streaming operators with functions that consume and produce Pandas dataframes as in the following example:

from streamz import Stream

def query(df):
    return df[ == 'Alice']

def aggregate(acc, df):
    return acc + df.amount.sum()

stream = Stream(), start=0)

This is fine, and straightforward to do if you understand streamz.core , Pandas, and have some skill with developing algorithms.

Streaming Dataframes

The streamz.dataframe module provides a streaming dataframe object that implements many of these algorithms for you. It provides a Pandas-like interface on streaming data. Our example above is rewritten below using streaming dataframes:

import pandas as pd
from streamz.dataframe import DataFrame

example = pd.DataFrame({'name': [], 'amount': []})
sdf = DataFrame(stream, example=example)

sdf[ == 'Alice'].amount.sum()

The two examples are identical in terms of performance and execution. The resulting streaming dataframe contains a .stream attribute which is equivalent to the stream produced in the first example. Streaming dataframes are only syntactic sugar on core streams.

Supported Operations

Streaming dataframes support the following classes of operations

  • Elementwise operations like df.x + 1

  • Filtering like df[ == 'Alice']

  • Column addition like df['z'] = df.x + df.y

  • Reductions like df.amount.mean()

  • Groupby-aggregations like df.groupby(

  • Windowed aggregations (fixed length) like df.window(n=100).amount.sum()

  • Windowed aggregations (index valued) like df.window(value='2h').amount.sum()

  • Windowed groupby aggregations like df.window(value='2h').groupby('name').amount.sum()

DataFrame Aggregations

Dataframe aggregations are composed of an aggregation (like sum, mean, …) and a windowing scheme (fixed sized windows, index-valued, all time, …)


Streaming Dataframe aggregations are built from three methods

  • initial: Creates initial state given an empty example dataframe

  • on_new: Updates state and produces new result to emit given new data

  • on_old: Updates state and produces new result to emit given decayed data

So a simple implementation of sum as an aggregation might look like the following:

from streamz.dataframe import Aggregation

class Mean(Aggregation):
    def initial(self, new):
        state = new.iloc[:0].sum(), new.iloc[:0].count()
        return state

    def on_new(self, state, new):
        total, count = state
        total = total + new.sum()
        count = count + new.count()
        new_state = (total, count)
        new_value = total / count
        return new_state, new_value

    def on_old(self, state, old):
        total, count = state
        total = total - old.sum()   # switch + for - here
        count = count - old.count() # switch + for - here
        new_state = (total, count)
        new_value = total / count
        return new_state, new_value

These aggregations can then used in a variety of different windowing schemes with the aggregate method as follows:




whose job it is to deliver new and old data to your aggregation for processing.

Windowing Schemes

Different windowing schemes like fixed sized windows (last 100 elements) or value-indexed windows (last two hours of data) will track newly arrived and decaying data and call these methods accordingly. The mechanism to track data arriving and leaving is kept orthogonal from the aggregations themselves. These windowing schemes include the following:

  1. All previous data. Only initial and on_new are called, on_old is never called.

    >>> df.sum()
  2. The previous n elements

    >>> df.window(n=100).sum()
  3. An index range, like a time range for a datetime index

    >>> df.window(value='2h').sum()

    Although this can be done for any range on any type of index, time is just a common case.

Windowing schemes generally maintain a deque of historical values within accumulated state. As new data comes in they inspect that state and eject data that no longer falls within the window.


Groupby aggregations also maintain historical data on the grouper and perform a parallel aggregation on the number of times any key has been seen, removing that key once it is no longer present.


In all cases, dataframe operations are only implemented with the .map and .accumulate operators, and so are equally compatible with core Stream and DaskStream objects.

Not Yet Supported

Streaming dataframe algorithms do not currently pay special attention to data arriving out-of-order.


As you have seen above, Streamz can handle arbitrarily complex pipelines, events, and topologies, but what if you simply want to run some Python function periodically and collect or plot the results?

streamz provides a high-level convenience class for this purpose, called a PeriodicDataFrame. A PeriodicDataFrame uses Python’s asyncio event loop (used as part of Tornado in Jupyter and other interactive frameworks) to call a user-provided function at a regular interval, collecting the results and making them available for later processing.

In the simplest case, you can use a PeriodicDataFrame by first writing a callback function like:

import numpy as np

def random_datapoint(**kwargs):
   return pd.DataFrame({'a': np.random.random(1)}, index=[])

You can then make a streaming dataframe to poll this function e.g. every 300 milliseconds:

df = PeriodicDataFrame(random_datapoint, interval='300ms')

df will now be a steady stream of whatever values are returned by the datafn, which can of course be any Python code as long as it returns a DataFrame.

Here we returned only a single point, appropriate for streaming the results of system calls or other isolated actions, but any number of entries can be returned by the dataframe in a single batch. To facilitate collecting such batches, the callback is invoked with keyword arguments last (the time of the previous invocation) and now (the time of the current invocation) as Pandas Timestamp objects. The callback can then generate or query for just the values in that time range.

Arbitrary keyword arguments can be provided to the PeriodicDataFrame constructor, which will be passed into the callback so that its behavior can be parameterized.

For instance, you can write a callback to return a suitable number of datapoints to keep a regularly updating stream, generated randomly as a batch since the last call:

def datablock(last, now, **kwargs):
    freq = kwargs.get("freq", pd.Timedelta("50ms"))
    index = pd.date_range(start=last + freq, end=now, freq=freq)
    return pd.DataFrame({'x': np.random.random(len(index))}, index=index)

df = PeriodicDataFrame(datablock, interval='300ms')

The callback will now be invoked every 300ms, each time generating datapoints at a rate of 1 every 50ms, returned as a batch. If you wished, you could override the 50ms value by passing freq=pd.Timedelta(“100ms”) to the PeriodicDataFrame constructor.

Similar code could e.g. query an external database for the time range since the last update, returning all datapoints since then.

Once you have a PeriodicDataFrame defined using such callbacks, you can then use all the rest of the functionality supported by streamz, including aggregations, rolling windows, etc., and streaming visualization.