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.