streamz | Real-time stream processing for python | Stream Processing library
kandi X-RAY | streamz Summary
kandi X-RAY | streamz Summary
Real-time stream processing for python
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Aggregate a windowed groupby
- Group DataFrame by groupby
- Add new values to accumulator
- Compute the values for each group
- Poll kafka
- Emit a new value
- Release reference n times
- Decrement the number of requests
- Accumulate the aggregator
- Map a function over each partition
- Emit a value
- Compute the difference between two datetimes
- Get a list of messages from kafka
- Convert to a list
- Calculate cumulative accumulator
- Visualize the stream
- Calculate a rolling accumulator
- Wrapper for IPython display
- Run the main loop
- Connect this node to the given downstream
- Aggregate the groupby sum
- Remove the upstream from the upstream
- Aggregate the groupby count
- Generate random values
- Compute the sum of each group
- Aggregate the mean of each group
streamz Key Features
streamz Examples and Code Snippets
Lines of Code : 11License : Permissive (MIT)
Copygit clone https://github.com/EthanRosenthal/gpu-streamz.git
cd gpu-streamz
conda env create -f environment.yml
conda activate gpu-streamz
# Install this streamz fork
pip install -e git+https://github.com/philippjfr/streamz.git@ced72b2583292decf928f from gpu_streamz import GPUMonitor
monitor = GPUMonitor()
monitor.start()
times = pd.date_range(start=13:00, end=15:00, freq=T)
for t in times:
df_instance = df[df["Time"]client.datasets["x"] = list_of_futures
def worker_function(...):
futures = get_client().datasets["x"]
data = get_client.gather(futures)
... work with data
import numpy as np
import holoviews as hv
import holoviews.plotting.bokeh
import streamz
import streamz.dataframe
renderer = hv.renderer('bokeh')
from holoviews import opts
from holoviews.streams import Pipe, Buffer
hv.extension('bokeh')
import pandas as pd
import holoviews as hv
from bokeh.models.renderers import GlyphRenderer
hv.extension('bokeh')
def apply_formatter(plot, element):
p = plot.state
# create secondary range and axis
p.extra_y_ranges = {"twiclass DataEmitter:
def __init__(self, pubsub, src):
self.pubsub = pubsub
self.src = src
self.thread = None
def emit_data(self, channel):
self.pubsub.subscribe(**{channel: self._handler})
self.thread = self.pubsub.run_ifrom streamz.dataframe import Random, StreamingDataFrame
params = {'freq': '2ms', 'interval': '50ms'}
source = Random(**params)
stream_df = (source * 0.5).cumsum()
sdf_params = {
'row': stream_df.x.rolling('100ms').mean(),
'veryCommunity Discussions
Trending Discussions on streamz
QUESTION
I am trying to figure out a correct way of processing streaming data using streamz. My streaming data is loaded using websocket-client, after which I do this:
ANSWER
Answered 2021-Mar-22 at 06:03I don't think websocket-client provides an async API and, so, it's blocking the event loop.
You should use an async websocket client, such as the one Tornado provides:
QUESTION
this is my first time asking something here, so I hope I am asking the following question the "correct way". If not, please let me know, and I will give more information.
I am using one Python script, to read and write 4000Hz of serial data to a CSV file.
The structure of the CSV file is as follows: (this example shows the beginning of the file)
...ANSWER
Answered 2020-Dec-21 at 12:49For the Googlers: I could not find a way to achieve my goal as described in the question.
However, if you are trying to plot live data, coming with high speed over serial comms (4000Hz in my case), I recommend designing your application as a single program with multiple processes.
The problem in my special case was, that when I tried to plot and compute the incoming data simultaneously in the same thread/task/process/whatever, my serial receive rate went down to 100Hz instead of 4kHz. The solution with multiprocessing and passing data using the quick_queue module between the processes I could resolve the problem.
I ended up, having a program, which receives data from a Teensy via serial communication at 4kHz, this incoming data was buffered to blocks of 4000 samples and then the data was pushed to the plotting process and additionally, the block was written to a CSV-file in a separate Thread.
Best, S
QUESTION
I have a pandas dataframe that includes timestamps, id, products, price and with more than 50+ columns.
I'd like to convert this data frame to a streaming data frame. For example, every 10 seconds, I'd like to receive 10 raws or 1 raw then after next 10 raws or 1 raw until the data frame ends.
I had a look to streamz library but couldn't find a proper function for this.
In this way, I am planning to apply some visualisation, and do some functional aggregations or further analysis.
...ANSWER
Answered 2020-Oct-06 at 14:40Previously I have gotten around a similar problem by using pd.date_range() to create times with the desired interval, then slicing the original dataframe by the times in the range.
For example.
QUESTION
Imports:
...ANSWER
Answered 2020-Feb-07 at 11:29"Why is that?": because the Dask distributed scheduler (which executes the stream mapper and sink functions) and your python script run in different processes. When the "with" block context ends, your Dask Client is closed and execution shuts down before the items emitted to the stream are able reach the sink function.
"Is there a nice way to otherwise check if a Stream still contains elements being processed": not that I am aware of. However: if the behaviour you want is (I'm just guessing here) the parallel processing of a bunch of items, then Streamz is not what you should be using, vanilla Dask should suffice.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install streamz
You can use streamz like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
