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Pandas
Pandas

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RapidMiner
RapidMiner

5
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0
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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.

- No public GitHub repository available -

What is Pandas?

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

What is RapidMiner?

It is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.
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        What are some alternatives to Pandas and RapidMiner?
        Panda
        Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
        NumPy
        Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
        R Language
        R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
        Anaconda
        A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
        SciPy
        Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
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        Decisions about Pandas and RapidMiner
        Guillaume Simler
        Guillaume Simler
        at Velchanos.io | 4 upvotes 57.6K views
        Jupyter
        Jupyter
        Anaconda
        Anaconda
        Pandas
        Pandas
        IPython
        IPython

        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

        See more
        Interest over time
        Reviews of Pandas and RapidMiner
        No reviews found
        How developers use Pandas and RapidMiner
        Avatar of Morris Clay
        Morris Clay uses PandasPandas

        Data wrangling, analysis and pre-processing

        Avatar of Eliana Abraham
        Eliana Abraham uses PandasPandas

        I used this a lot more than I used Jupyter.

        Avatar of GadgetSteve
        GadgetSteve uses PandasPandas

        Great data manipulation tool

        How much does Pandas cost?
        How much does RapidMiner cost?
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