I was reading about how functions become bound methods when being set as class atrributes. I then observed that this is not the case for functions that are wrapped by functools.partial. What is the explanation for this? Simple example: I kind of expected them both to behave in the same way. Answer The trick that allows functions to become bound
Tag: functools
How do I use python reduce function to fill NaN values
I have a dataframe with missing data in several columns. In some of these columns, say ‘Col_A’ to ‘Col_D’, I’d like to replace them with 0. I tried it this way: reduce(lambda x : df.fillna({x : 0}, …
@lru_cache decorator excessive cache misses
How can you configure lru_cache to key its cache based on actual values received, rather than how the function was called? >>> from functools import lru_cache >>> @lru_cache … def …
Does python functools.singledispatch work with Generator type?
I extended the example at https://docs.python.org/3/library/functools.html#functools.singledispatch by adding a registration for generator type from functools import singledispatch from typing import …
Can One Replace or Remove a specific key from functools.lru_cache?
I’m using a functools.lru_cache to serve temp file paths given certain input*. However in case the path no longer exists, I would like to remove/replace the single corresponding key. The cache_clear() …
Python functools lru_cache with instance methods: release object
How can I use functools.lru_cache inside classes without leaking memory? In the following minimal example the foo instance won’t be released although going out of scope and having no referrer (other than the lru_cache). But foo and hence foo.big (a BigClass) are still alive That means that Foo/BigClass instances are still residing in memory. Even deleting Foo (del Foo) will