Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Cloudify
OpenShift
Kubernetes
Heroku
Morpheus
Microsoft Azure
Apache Mesos
Redis
Cloudify provides infrastructure automation using โEnvironment as a Serviceโ technology to deploy and continuously manage any cloud, private data center, or Kubernetes service from one central point while leveraging existing toolchains; Terraform, Ansible, and more. Use Cloudify to import existing automation templates and scripts and automatically convert them into certified environments. Manage them using the Cloudify console or export these environments to ServiceNow and enable users to deploy, continuously manage and maintain them as part of approval workflows.
Key Values: - Speed up deployments of your Test/Dev/Production environments. - Manage customers' heterogeneous cloud environments. - Enable Continuous Updates (Day-2) for your Production environments. - A clean API to work on top of all your tools that can easily be used within ServiceNow. - Manage Kubernetes clusters at scale.
Matplotlib CloudifyBased on our record, Matplotlib seems to be a lot more popular than Cloudify. While we know about 114 links to Matplotlib, we've tracked only 2 mentions of Cloudify. 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.
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 / 4 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 / 7 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
Cloudify looks interesting if you can stand the price, depends how badly you need the features it offers. Source: about 4 years ago
Cloudify is a platform that automates and manages entire lifecycles of an application or network service. Source: over 4 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
OpenShift - OpenShift gives you all the tools you need to develop, host and scale your apps in the public or private cloud. Get started today.
NumPy - NumPy is the fundamental package for scientific computing with Python
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.