CODEWITHSUNDEEP
pythonscikit-learn
Atul Kapisway
Atul Kapisway

Reputation: 21

StandardScaler.fit() displaying value error

I was using StandardScaler to scale my data as was shown in a tutorial. But its not working.

I tried copy the same code as was used in the tutorial but still error was displayed.

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(df.drop('TARGET CLASS',axis=1))
scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))

The error is as follows:

 TypeError: fit() missing 1 required positional argument: 'X'

Upvotes: 2

Views: 747

Answers (1)

Luca Massaron
Luca Massaron

Reputation: 1809

By trying to recreate your problem, it seems that everything in the code is correct and being executed perfectly. Here is a stand-alone example I created in order to test your code:

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler

data = load_iris()
df = pd.DataFrame(data.data, columns=['TARGET CLASS', 'a', 'b', 'c'])

scaler = StandardScaler()
scaler.fit(df.drop('TARGET CLASS', axis=1))
scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))

I suggest you examine your variable df by printing it. For instance, you could try to transform it into a NumPy array before passing it and print its contents:

import numpy as np

X = df.drop('TARGET CLASS',axis=1).values
print(X)

Upvotes: 1

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