Plotly Dash vs. PyQtGraph vs. Python RPA vs. dashDB Local Comparison Chart

PyQtGraph is a pure-python graphics and GUI library built on PyQt/PySide and NumPy. It is intended for use in mathematics/scientific/engineering applications. Despite being written entirely in python, the library is very fast due to its heavy leverage of NumPy for number crunching and Qt's GraphicsView framework for fast display. PyQtGraph is distributed under the MIT open-source license. Basic 2D plotting in interactive view boxes. Line and scatter plots. Data can be panned/scaled by mouse. Fast drawing for real-time data display and interaction. Displays most data types (int or float; any bit depth; RGB, RGBA, or luminance). Functions for slicing multidimensional images at arbitrary angles (great for MRI data). Rapid update for video display or real-time interaction. Image display with interactive lookup tables and level control. Mesh rendering with isosurface generation. Interactive viewports rotate/zoom with mouse. Basic 3D scenegraph for easier programming.

As the newest edition to the IBM dashDB family, dashDB Local rounds out IBM's hybrid data warehouse strategy, providing organizations the most flexible architecture needed to lower the cost model of analytics in the dynamic world of big data and the cloud. How is this possible? Through a common analytics engine, with different deployment options across private and public clouds, analytics workloads can be moved and optimized with ease. dashDB Local is now an option when you prefer deployment on a hosted private cloud or on-premises private cloud through a software-defined infrastructure. From an IT standpoint, dashDB Local simplifies deployment and management through container technology, with elastic scaling and easy maintenance. From a user standpoint, dashDB Local provides the speed needed to quickly cycle through the process of data acquisition, applies the right analytics to meet a specific use case, and operationalizes the insights.