Analysts often use tools like Pandas, Scikit-Learn, Numpy, and the rest of the Python ecosystem to analyze data on their personal computer. They like these tools because they are efficient, intuitive, and widely trusted. However, when they choose to apply their analyses to larger datasets, they find that these tools were not designed to scale beyond a single machine. And so, the analyst rewrites their computation using a more scalable tool, often in another language altogether. This rewrite process slows down discovery and causes frustration. Dask provides ways to scale Pandas, Scikit-Learn, and Numpy workflows more natively, with minimal rewriting. It integrates well with these tools so that it copies most of their API and uses their data structures internally. Moreover, Dask is co-developed with these libraries to ensure that they evolve consistently, minimizing friction when transitioning from a local laptop, to a multi-core workstation, and then to a distributed cluster. Analysts familiar with Pandas/Scikit-Learn/Numpy will be immediately familiar with their Dask equivalents, and have much of their intuition carry over to a scalable context.

import sectorstudies