KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.
$0
per month
PyCharm
Score 9.2 out of 10
N/A
PyCharm is an extensive Integrated
Development Environment (IDE) for Python developers. Its
arsenal includes intelligent code completion, error detection, and rapid
problem-solving features, all of which aim to bolster efficiency. The product supports programmers in composing orderly and maintainable
code by offering PEP8 checks, testing assistance, intelligent refactorings, and
inspections. Moreover, it caters to web development frameworks like Django and
Flask by providing framework…
$9.90
per month per user
Python IDLE
Score 8.5 out of 10
N/A
Python's IDLE is the integrated development environment (IDE) and learning platform for Python, presented as a basic and simple IDE appropriate for learners in educational settings.
Python IDLE is very easy to use compared to PyCharm. So for simple python scripting, Python IDLE is preferable to PyCharm, which has relatively steep learning curve. Compared to Python IDLE, PyCharm is more resource intensive, which may be worth it when comes to large projects, …
I chose python IDLE for its simplicity and ease of use, which made it ideal for rapid prototyping and small scale development future sets: while python IDLE offers a basic set of features, including syntax highlighting, auto completion and basic debugging tools Performance …
KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.
PyCharm is well suited to developing and deploying Python applications in the cloud using Kubernetes or serverless pipelines. The integration with GitLab is great; merges and rebates are easily done and help the developer move quickly. The search engine that allows you to search inside your code is also great. It is less appropriate for other languages.
Scenarios where python IDLE is well suited 1-Quick scripting and prototyping 2-Education and training 3-small projects utilities 4-exploring python libraries and modules Scenarios where python is less appropriate 1 large scale projects 2 complex debugging and profiling 3 multi language development 4 Advanced code analysis and inspection
Git integration is really essential as it allows anyone to visually see the local and remote changes, compare revisions without the need for complex commands.
Complex debugging tools are basked into the IDE. Controls like break on exception are sometimes very helpful to identify errors quickly.
Multiple runtimes - Python, Flask, Django, Docker are native the to IDE. This makes development and debugging and even more seamless.
Integrates with Jupyter and Markdown files as well. Side by side rendering and editing makes it simple to develop such files.
The biggest complaint I have about PyCharm is that it can use a lot of RAM which slows down the computer / IDE. I use the paid version, and have otherwise found nothing to complain about the interface, utility, and capabilities.
We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end
KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows
It's pretty easy to use, but if it's your first time using it, you need time to adapt. Nevertheless, it has a lot of options, and everything is pretty easy to find. The console has a lot of advantages and lets you accelerate your development from the first day.
The IDE Python IDLE is a good place to start as it helps you become familiar with the way Python works and understand its syntax.
This IDE allows you to configure the environment, font, size, colors, .....
It also looks like any simple text editor for any operating system, I work with Windows or Linux interchangeably, and you don't have to learn to use the IDE before programming.
Once the IDE is executed you can start programming directly in it.
KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.
I rate 10/10 because I have never needed a direct customer support from the JetBrains so far. Whenever and for whatever kind of problems I came across, I have been able to resolve it within the internet community, simply by Googling because turns out most of the time, it was me who lacked the proper information to use the IDE or simply make the proper configuration. I have never came across a bug in PyCharm either so it deserves 10/10 for overall support
Python IDLE support is what the community can give you. As it is free software, it does not have support provided by the manufacturer or by third-parties.
In any case, for most of the problems that normal users can find, the solution, or alternatives, can be found quickly online.
As this IDE is made in Python, the support is the same group of Python developers.
KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.
Having used both the Alteryx and [KNIME Analytics] I can definitely feel the ease of using the software of Alteryx. The [KNIME Analytics] on the other hand isn't that great but is 90% of what Alteryx can do along with how much ease it can do. Having said that, the 90% functionality and UI at no cost would be enough for me to quit using Alteryx and move towards [KNIME Analytics].
When it comes to development and debugging PyCharm is better than Spyder as it provides good debugging support and top-quality code completion suggestions. Compared to Jupiter notebook it's easy to install required packages in PyCharm, also PyChram is a good option when we want to write production-grade code because it provides required suggestions.
It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop
It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.
