A Functional Programming Approach to Data Processing in Python - 1 of 2
summary
Claims a lightweight, functional alternative to Pandas and shows concrete code that lazily parses and processes real GitHub JSON results. Uses urllib/json, ijson+itertools generators to stream items, operator.itemgetter with sorted+groupby for grouping, itertools.count/islice/repeat to simulate infinite and finite streams, and functools.lru_cache for LRU cache memoization. Binder notebooks and exercises are provided; meza is presented as meza: lazy, pure functional data library that reads files lazily and demonstrates the approach working on examples.
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chapters (10)
- 0:00Start
- 11:22Reading data (naive)
- 14:16Processing data (naive)
- 19:36Reading data (lary evaluation)
- 21:11Processing data (lary evaluation)
- 26:48Processing data dary evaluation
- 33:15Grouping data
- 36:19Processing expensive data (memoiration)
- 50:34Exercise #1: Problem
- 52:39Exercise #1: Jupiter Notebook
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