Perform k-means clustering over multiple columns
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:

import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans

X, _ = make_blobs(n_samples=10, centers=3, n_features=4)

df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])

kmeans = KMeans(n_clusters=3)

y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])

df['Cluster'] = y

print(df.head())


Which outputs:

     Feat_1    Feat_2    Feat_3    Feat_4  Cluster
0  0.005875  4.387241 -1.093308  8.213623        2
1  8.763603 -2.769244  4.581705  1.355389        1
2 -0.296613  4.120262 -1.635583  7.533157        2
3 -1.576720  4.957406  2.919704  0.155499        0
4  2.470349  4.098629  2.368335  0.043568        0