Score output based on multiple pandas dataframe columns - pandas

Question

What am I trying to do?
I'm trying to generate a total_score column based on two other columns.
The level_count column will have a min value of 1 and max of 3
The range_bins column will have a min value of low and max of very_high
In order to sum I created a temp range_score column from 1 to 4. Is there a better way than creating this temp column?
How do I normalise the scores so level_count and range_bins have the same weighting even though the range of each column differs? (3 values vs 4)
Data
data = { 'level_count': {0: 2, 1: 2, 2: 3, 3: 1},
'range_bins': {0: 'high', 1: 'medium', 2: 'low', 3: 'very_high'}}
df = pd.DataFrame(data)
df["range_score"] = df.range_bins.replace({"low": 1, "medium": 2,"high":3,"very_high":4})
df["total_score"] = df[["level_count","range_score"]].sum(axis=1)
drop temp column and show output:
df.drop(columns= "range_score")
level_count range_bins total_score
0 2 high 5
1 2 medium 4
2 3 low 4
3 1 very_high 5
Desired output
Rows 2 and 3 have equal importance and the total_score should reflect this. I may also need to add other similar columns with maybe only two categories in a similar way.

Answer 1

To achieve equal weighting of row 2 and 3, you could create a function that takes a row from the dataframe and then write down the entire logic. You can then apply this function to the dataframe with df.apply. Since I cannot tell the complete logic from your description, I can only provide half the solution - You will have to write down the complete logic.
def total_score(row):
if row.level_count == 1 & row.range_bins == 'very_high':
return 4
elif row.level_count == 2 & row.range_bins == 'very_high':
return ?
elif row.level_count == 2 & row.range_bins == 'very_high':
return ?
df['total_score'] = df.apply(lambda x: total_score(x))