CODEWITHSUNDEEP
pythonpandasscikit-learn
Leon.L
Leon.L

Reputation: 13

How to keep index of a dataframe while using minmaxscaler?

I'm trying to implement entropy method. So, I need to use minmaxscaler on a dataframe which convert from a dict. However, because I don't know how many parameters will be used in the values of the dict, I couldn't name all the rows while using DataFrame.from_dict(). As a result, while I used the MinMaxScaler, the DataFrame turns into a ndarray, and the index of the dict disappears. How could I keep the index?

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

dict = {'A':[89, 11], 'B':[80, 96], 'C':[97, 89], 'D':[90, 24], 'E': [100, 90]}

df = pd.DataFrame.from_dict(dict, orient='index')
print(df)
scaler = MinMaxScaler()
df = scaler.fit_transform(df[0:])
print(df)
yij = df.apply(lambda x: x / x.sum(), axis=0) # An error occurs here: AttributeError: 'numpy.ndarray' object has no attribute 'apply'
K = 1/np.log(len(df))
tmp = yij*np.log(yij)
tmp=np.nan_to_num(tmp)
ej = -K*(tmp.sum(axis=0))
wj = (1 - ej) / np.sum(1 - ej)
score = yij.apply(lambda x: np.sum(100 * x * wj), axis=1)
print(score)

Upvotes: 0

Views: 2220

Answers (1)

Antoine Dubuis
Antoine Dubuis

Reputation: 5304

You can use:

df[:] = scaler.fit_transform(df)

which will replace all values by their transformed values. This operation also keep the rows and columns indexes.

Upvotes: 3

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