Jupyter Notebook is a great tool for data science prototyping, visualization and sharing. The first code cell of every notebook I work on contains the following commands: %matplotlib inline %load_ext autoreload %autoreload 2 These are some of the IPython Magic commands – handy enhancements added on top of the standard Python syntax to solve various tasks specific to interactive computing. The first line in the snippet above configures the notebook session to visualize and store all the Matplotlib figures inside the notebook.
Python works great together with native code. The success of the Python data science stack is largely attributed to the capability of building native extensions. It is no secret, however, that the out-of-the-box C-based facilities are rather cumbersome to use. Luckily, there are a bunch of alternatives out there. The one I liked the most is Pybind11, a fork of Boost.Python with focus on the modern C++ standards. It is distributed as a lightweight header-only library that can be bundled with your project.
When it comes to building and installing OpenCV with Python support on *nix platforms, the collection of tutorials by Adrian Rosebrock is the best. He provides detailed description of the required steps, as well as motivation for better development practices. In particular, Adrian creates a dedicated virtualenv environment, installs NumPy in it, and builds the whole thing having this environment activated. Such a solution worked pretty well for me, both on Linux and macOS.