Reactive programming is a radically effective approach to compose data as queryable, live-time streams
Not only can you concisely wrangle and analyze static snapshots of data, but also real-time infinite data (e.g. stock quotes, Twitter streams)
There is growing interest in wrangling and analyzing live data streams, and RxPy is a lightweight library that achieves this
Data does not have to be modeled as something static, but rather something that is constantly in motion
Data analysis professionals should strive to create code that can plug into existing systems easily, as well as be reused and evolve with the business
import rx from rx import operators as ops s1 = rx.from_(['A', 'B', 'C', 'D', 'E', 'F']) s2 = rx.range(1,7) stream = rx.zip(s1, s2) stream.subscribe(lambda s: print(s[0], s[1]))
letters = rx.from_(['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon']) intervals = rx.interval(1) stream = rx.zip(letters, intervals) stream.subscribe(lambda s: print(s)) input('Press any key to quit')
items = ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon'] source = rx.from_(items) stream = source.pipe( ops.group_by(lambda s: len(s)), ops.flat_map(lambda g: g.pipe( ops.to_list() )), ) stream.subscribe(lambda s: print(s))