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Showing posts from November, 2018

Visualizing large scale Uber Movement Data

New York's cab data visualization from Uber's Engineering blog Last month one of my acquaintances in LinkedIn pointed me to a very interesting dataset. Uber's Movement Dataset. It was fascinating to explore their awesome GUI and to play with the data. However, their UI for exploring the dataset leaves much more to be desired, especially the fact that we always have to specify source and destination to get relevant data and can't play with the whole dataset. Another limitation also was, the dataset doesn't include any time component. Which immediately threw out a lot of things I wanted to explore. When I started looking out if there is another publicly available dataset, I found  one  at Kaggle. And then quite a few more at Kaggle. But none of them seemed official, and then I found  one released  by NYC - TLC which looked pretty official and I was hooked. To explore the data I wanted to try out OmniSci. I recently saw a video of a  talk  at jupytercon by Randy Zw