Pandas vs RapidMiner: What are the differences?
Developers describe Pandas as "High-performance, easy-to-use data structures and data analysis tools for the Python programming language". Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. On the other hand, RapidMiner is detailed as "Data Science, Reimagined. Prep data, create predictive models & operationalize analytics within any business process". RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
Pandas and RapidMiner belong to "Data Science Tools" category of the tech stack.
Some of the features offered by Pandas are:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
On the other hand, RapidMiner provides the following key features:
- Graphical user interface
- Analysis processes design
- Multiple data management methods
Pandas is an open source tool with 20.7K GitHub stars and 8.16K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
What is Pandas?
What is RapidMiner?
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Why do developers choose RapidMiner?
What are the cons of using Pandas?
What are the cons of using RapidMiner?
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Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead