In Python data analysis, how do I use caching?

Caching is an essential technique in data analysis to improve performance by storing intermediate results and reusing them when the same computations are needed. In Python, caching can be implemented using various libraries such as `functools.lru_cache`, `diskcache`, or even by using database solutions.

caching, data analysis, performance improvement, Python, functools
Learn how to effectively use caching in Python data analysis to enhance the efficiency of your computations and reduce processing time.
from functools import lru_cache @lru_cache(maxsize=None) def expensive_computation(x): # Simulate an expensive computation return sum(i * i for i in range(x)) # Example usage of cached function result1 = expensive_computation(10000) result2 = expensive_computation(10000) # This will use the cached result

caching data analysis performance improvement Python functools

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