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Q:

Preventing inverse dependence or double counting of score

In my application, I want to assign scores to multiple features and determine the most important features based on that score. The application is able to sort by score, but for some reason, the scores are being inverted, so for one feature with a high score, this feature is considered the most important feature, but for another, the most important feature has a lower score.
I’ve included an example of the code below:
from random import randint
import itertools
import pandas as pd
import numpy as np

df = pd.DataFrame(randint(1,100,5),
columns=[‘A’,’B’,’C’,’D’,’E’],
dtype = np.int64)

for col in df.columns:
scores = np.array(range(10))
for (x,y) in itertools.product(df.iloc[:, col].values, scores):
df.loc[(df[col] == y), col] = x

df

#[[ 5 10 2 3]
# [ 77 87 1 89]
# [ 8 6 0 2]
# [ 91 81 83 82]
# [ 5 7 5 5]
# [ 9 14 8 7]]

If you think that the example has a solution, I would appreciate an explanation of what’s going on because I can’t wrap my head around how that would work.
Thanks!

A:

This is because in your code, you reset col on each pass through the loop:
for (x,y) in itertools.product(df.iloc[:, col].values, scores):

You don’t store this in a variable, so it’s being reset on every pass through the loop. That’s the problem. To fix this, do this:
scores_trans = list(itertools.product(df.iloc[:,
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