Hangfire
Sidekiq
Resque
Histats
AFSAnalytics
Matomo
Woopra
KISSmetrics
Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Hangfire MatplotlibBased on our record, Matplotlib seems to be a lot more popular than Hangfire. While we know about 114 links to Matplotlib, we've tracked only 5 mentions of Hangfire. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Hangfire (https://hangfire.io) includes default exception handling and is very extensible, I think it's a good mid-level choice and a good alternative to other queue mechanism, if you can't afford to host a separated queue service or can't manage a separated service; also scales pretty well (you can have multiple servers handling the same background job queue, or different queues). It runs on Sql Server and MySql... Source: about 4 years ago
I used to just use hangfire.io in .net and worked wonderfully for any long running tasks or schedules. Had a great queuing system, UI to know if they failed , etc. That's how I'd send emails, pdf's, and other things along that nature. Then if it were more just a db related operation, just setup a schedule in mssql job service. Source: about 4 years ago
You can use hangfire for cronjob, to run at a time in future, you can use Hangfire.Schedule(jobid, datetime). Source: about 4 years ago
So another option is to use something like https://hangfire.io to pull the jobs and process them? Source: over 4 years ago
I've got a fairly large process I need to handle in background on my .net core web app so I've exported it to a background task using Hangfire. Source: about 5 years ago
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 8 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 10 months ago
Sidekiq - Sidekiq is a simple, efficient framework for background job processing in Ruby
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Resque - Resque is a Redis-backed Ruby library for creating background jobs, placing them on multiple queues, and processing them later.
NumPy - NumPy is the fundamental package for scientific computing with Python
Histats - Start tracking your visitors in 1 minute!
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
