Megaladata vs. NumPy vs. Python vs. Scoop Analytics Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

Scoop is the best thing to happen to data since the spreadsheet. Unlock all of your business data in an instant. Scoop lets you combine and analyze data from anywhere to power better decisions. Forget all the tools and resources it takes to get answers out of your data. Scoop takes you from data collection to spreadsheet analysis to visual data stories, all in a single platform. Automatically collect data from your business applications to combine and blend together. Any application can push data into Scoop, with no limited pre-built connectors or custom APIs. Create master datasets to fuel analytics, visualizations, and more using your existing spreadsheets. Scoop automatically keeps your data up-to-date. Go from scooping up application data to prepping datasets in spreadsheets to analyzing and sharing visual data stories. No technical setup or data team is needed. Blend data from different sources and tell beautiful data stories based on live, sharable data.