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Pandas vs KNIME: 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, KNIME is detailed as "Create and productionize data science using one easy and intuitive environment". It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

Pandas and KNIME can be categorized as "Data Science" tools.

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, KNIME provides the following key features:

  • Access, merge, and transform all of your data
  • Make sense of your data with the tools you choose
  • Support enterprise-wide data science practices

Pandas is an open source tool with 25.9K GitHub stars and 10.6K GitHub forks. Here's a link to Pandas's open source repository on GitHub.

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Pros of KNIME
Pros of Pandas
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    What is KNIME?

    It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.

    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.

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    What are some alternatives to KNIME and Pandas?
    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.
    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.
    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.
    Dataform
    Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
    PySpark
    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
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