Is there a function to normalize strings and convert them to integers/floats?

Question

I have multiple lists of features which are strings that I want to analyze. That is, e.g.:

[["0.5", "0.4", "disabled", "0.7", "disabled"], ["feature1", "feature2", "feature4", "feature1", "feature3"]]

I know how to convert strings like "0.5" to floats, but is there a way to "normalize" such lists to integer or float values (each list independently in my case)? I would like to get something like this:

[[2, 1, 0, 3, 0], [0, 1, 3, 0, 2]]

Does anyone know how to achieve this? Unfortunately I couldn't to find anything related to this problem yet.

Answer 1

Use a dictionary and a counter to give IDs to new values and remember past IDs:

import itertools, collections

def norm(lst):
    d = collections.defaultdict(itertools.count().__next__)
    return [d[s] for s in lst]

lst = [["0.5", "0.4", "disabled", "0.7", "disabled"],
       ["feature1", "feature2", "feature4", "feature1", "feature3"]]
print(list(map(norm, lst)))
# [[0, 1, 2, 3, 2], [0, 1, 2, 0, 3]]

Or by enumerating sorted unique values; note, however, that "disables" sorts after the numeric values:

def norm_sort(lst):
    d = {x: i for i, x in enumerate(sorted(set(lst)))}
    return [d[s] for s in lst]

print(list(map(norm_sort, lst)))
[[1, 0, 3, 2, 3], [0, 1, 3, 0, 2]]