Normalize the number of rows based on min and max of that rows in dataframe-CodePudding

I have a data frame and I want to normalize each number based on the minimum of that row and the maximum of that row based on this formulation.

  x_normalized = (x_unnormalized-x_min)/(x_max-x_min).

I've check the scikit-learn package and I could not find any function for that. Could you help me with this? I also provide a sample as follows and what I want.

import pandas as pd
import numpy as np


df = pd.DataFrame()
df['id'] = [a, b, c]
df['c1'] = [2, 5, 3]
df['c2'] = [0, 5, 6]
df['c3'] = [8, 7, 9]

print(df)
#here is the dataframe which i want
df = pd.DataFrame()
df['id'] = [a, b, c]
df['c1'] = [1/4, 0, 0]
df['c2'] = [0, 0, 0.5]
df['c3'] = [1, 1, 1]
df

CodePudding user response：

It looks like there is a typo in your output.

You can use simple vectorial operations:

def norm(df):
    MIN = df.min(1)
    MAX = df.max(1)
    return df.sub(MIN, 0).div(MAX-MIN, 0)
    
df2 = norm(df)

output:

     c1   c2   c3
0  0.25  0.0  1.0
1  0.00  0.0  1.0
2  0.00  0.5  1.0

axis-aware version:

def norm(df, axis=1):
    MIN = df.min(axis)
    MAX = df.max(axis)
    return df.sub(MIN, 1-axis).div(MAX-MIN, 1-axis)
    
norm(df, axis=0)

output:

         c1        c2   c3
0  0.000000  0.000000  0.5
1  1.000000  0.833333  0.0
2  0.333333  1.000000  1.0