Ranking based on Weighted Average for multiple criteria-CodePudding

Given a list of cars and their top speeds, MPG and car cost. I want to rank them. With Speed a 'weight' of 50%, MPG 'weight' of 30% and car cost 20%.

The faster the car, the better.. The higher the MPG, the better... The lower the COST, the better...

What formula can I use to rank the cars in order, based on this criteria?

So given this list. How can I rank them?

CAR     SPEED      MPG      COST 
A        135       20       50,000
B        150       15       60,000
C        170       18       80,000
D        120       30       40,000

What software library can i use to do this ? Python? Numpy?

thanks

CodePudding user response：

You can score the different rows with simple arithmetic. Since COST is inversely proportioned, we'll consider 1/COST instead. If COST can ever be zero, you could also subtract it instead of use the inverse.

import pandas as pd

df = pd.DataFrame({'CAR': ['A', 'B', 'C', 'D'],
                   'SPEED': [135, 150, 170, 120],
                   'MPG': [20, 15, 18, 30],
                   'COST': [50.0, 60.0, 80.0, 40.0]})


score = 0.5*df["SPEED"]   0.3*df["MPG"]   0.2/df["COST"]
df.loc[score.sort_values(ascending=False).index]

  CAR  SPEED  MPG  COST
2   C    170   18  80.0
1   B    150   15  60.0
0   A    135   20  50.0
3   D    120   30  40.0

CodePudding user response：

You should use a Python library called "pandas", where you can manipulate your data, Try something like this:

import pandas as pd

df =  pd.read_csv("your_csv_file.csv")

scores = []
for index, row in df.iterrows():
    mpg = row['MPG']
    speed = row['SPEED']
    cost = row['COST']
    score = (int(mpg) / df['MPG'].max() * 50)   (int(speed) / df['SPEED'].max() * 30)   (df['COST'].max() / int(cost) * 20)
    scores.append(score)
df['SCORE'] = scores
df = df.sort_values(by=['SCORE'], ascending=False)

It creates a new column with its associated score:

  CAR  SPEED  MPG  COST       SCORE
3   D    120   30  40.0  111.176471
0   A    135   20  50.0   89.156863
2   C    170   18  80.0   80.000000
1   B    150   15  60.0   78.137255