🥱 Lazy Processing in Python
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Welcome back to Pandas Daily! Your daily 5-minute boost to becoming confident in Python. 🔗 The Merging Problem (itertools) Let's say you have two separate data streams, transaction amount - offline and online . And you need to process them as one continuous flow.
sales_offline = [240, 180]
sales_online = [870, 910] Standard Python will combine and create a new list. That eats memory (imagine if there are thousands of transactions). Here comes
In:
from itertools import chain sales_overall = chain(sales_offline, sales_online) for log in sales_overall: print(log)
Out:
240 180 870 910 More use cases... ⌛ First load important functions of itertools
In:
from itertools import count, cycle, repeat, chain, accumulate
🌱 Grow list dynamically Log customer ID (stating from 10) as they are generated
In:
c = count(10) print([next(c) for _ in range(5)])
Out: [10, 11, 12, 13, 14]
🔄 Loop a sequence
In:
cy = cycle(["A", "B"]) print([next(cy) for _ in range(6)])
Out: ['A', 'B', 'A', 'B', 'A', 'B']
🎯 Repeat same values Example: Use as placeholders if some data is missing
In:
print(list(repeat("x", 4))))
Out: ['x', 'x', 'x', 'x']
✨ Add numbers real-time Example: Total store sale till afternoon
In:
tx = [120, -30, 50, -10] print(list(accumulate(tx)))
Out: [120, 90, 140, 130]
📈 Monitor highest value Example: Costliest product sold till now
In:
metrics = [300, 250, 420, 380, 500] print(list(accumulate(metrics, max)))
Out: [300, 300, 420, 420, 500]
⭐📣 That's it for today! If you liked it, please share it with anyone who will find it useful and share your feedback below 🐼
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