How to get random selected values from dictionary

Question

I am having a directory that contains an N number of key and values and I want to iterate only 4 random and unique keys and values and then need to check the highest and lowest key and values among 4 entries then find the subtraction.

let consider:

 main_dict = {"a":144,"c":55,"d":33,"e":65,"f":44,"s":344,"r":90,"t":33} random_key_and_values = 4

the new dictionary will be

 new_dict = {"c":55,"f":44,"r":90,"d":33}

with the new dictionary, I need the subtraction of the highest and lowest values of new_dict values.

 output = 57

Please accept an answer using the grey checkmark on the left of it. — TheEagle
– TheEagle, Commented Apr 25, 2021 at 19:24

TheEagle · Accepted Answer · 2021-04-25 10:28:01Z

@tino's answer may work, but it can be done without third-party libraries, just with builtins:

import random main_dict = {"a" : 144, "c" : 55, "d" : 33, "e" : 65, "f" : 44, "s" : 344, "r" : 90, "t" : 33} rand_choice_len = 4 new_dict = dict(random.choices(list(main_dict.items()), k=rand_choice_len)) output = max(new_dict.values()) - min(new_dict.values()) print(new_dict, "difference of highest and lowest is:", output)

References:
random.choices
max
min dict

You can use dict(...) instead of {k: v for k, v in ...} - the dict constructor accepts an iterable of pairs.
@kaya3 thanks, weird I didn't encounter that when searching for "python dict from items" …

tino · Accepted Answer · 2021-04-25 09:45:45Z

The code below should be what you are looking for. A couple loops and np.random.choice

import numpy as np main_dict = {"a":144,"c":55,"d":33,"e":65,"f":44,"s":344,"r":90,"t":33} random_key_and_values = 4 new_dict = { key : main_dict[key] for key in np.random.choice(np.array(list(main_dict.keys())),random_key_and_values)} diff = np.array(list(new_dict.values())).max() - np.array(list(new_dict.values())).min() print(diff)

Using numpy can be handy sometimes, but sometimes, it can be an overkill ...
why is that? Never really understood this "overkill" idea. Numpy is rather standard in every python distribution, why not using it?

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