NumPy vs KNIME: What are the differences?

Developers describe NumPy as "Fundamental package for scientific computing with Python". 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. 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.

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

Some of the features offered by NumPy are:

• a powerful N-dimensional array object
• sophisticated (broadcasting) functions
• tools for integrating C/C++ and Fortran code

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

NumPy is an open source tool with 14.4K GitHub stars and 4.73K GitHub forks. Here's a link to NumPy's open source repository on GitHub.