It is said that, if you stand at Times Square long enough, you will meet everyone in the world. By now, you must have been convinced that NumPy is the Times Square of SciPy. If you are writing scientific applications in Python, there is not much you can do without digging into NumPy. Figure 2 shows the scope of SciPy in scientific computing at varying levels of abstraction. The red arrow denotes the various low-level functions that are expected of scientific software, and the blue arrow denotes the different application domains that exploit these functions. Python, armed with the SciPy stack, is at the forefront of the languages that provide these capabilities.
A Google Scholar search for NumPy returns nearly 6,280 results. Some of these are papers and articles about NumPy and the SciPy stack itself, and many more are about NumPy's applications in a wide variety of research problems. Academics love Python, which is showcased by the increasing popularity of the SciPy stack as the primary language of scientific programming in countless universities and research labs all over the world. The experiences of many scientists and software professionals have been published on the Python website:
