Pandas normalize dataframe by iterating over all columns but index

Question

I need to perform a normalization of a dataframe, contatining one index column and other columns with numeric values.

Index    a     b      c
xy1     555   436    3667
xz2    4626   658    463
xr3     425   674    436
bx4    4636   6567   6346

I want to perform max-min normalization on the dataframe, drop columns containing NaNs, and return the normalized dataframe with the original index. I'm thinking of something like this, but how can I exclude the index column from the loop, so that it stays the same in the returned dataframe?

def normalize(df):
    result = df.copy()
    for feature_name in df.columns:
        max_value = df[feature_name].max()
        min_value = df[feature_name].min()
        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
        if result[feature_name].isnull().values.any():
            result.drop([feature_name], axis=1, inplace=True)
            print(f'Something wrong in {feature_name}, dropping this feature.')
    return result

Answer 1

You can simplify your implementation of min-max scaling:

s = df.set_index('Index').dropna(axis=1)
s = (s - s.min())  / (s.max() - s.min())

Or, you can use MinMaxScaler from sklearn.preprocessing :

from sklearn.preprocessing import MinMaxScaler

s = df.set_index('Index').dropna(axis=1)
s[:] = MinMaxScaler().fit_transform(s)

---

print(s)

              a         b         c
Index                              
xy1    0.030872  0.000000  0.546701
xz2    0.997625  0.036209  0.004569
xr3    0.000000  0.038819  0.000000
bx4    1.000000  1.000000  1.000000