K-means anomaly detection scatter plot

The following code, takes a single column from a dataset and then adds 50 anomalies to the dataset that is quite bigger than the maximum values of the dataset.

```
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns

X=pd.read_csv('C:/Files/dataset.csv’, sep=';', encoding='latin1' )

#Adding the anomalies

for i in range(0, 50):
    X.append(X.my_column.max() * (10 + pd.np.abs(pd.np.random.normal())))
X = pd.np.array(X)

clf = KMeans(n_clusters=2, init='k-means++', max_iter=300, n_init=10, random_state=1)
clf.fit(X.my_column.values.reshape(-1, 1))
X_prd = clf.predict(X.my_column.values.reshape(-1, 1))

plt.scatter(X.index, X.my_column, c=X_prd)

```
[![enter image description here][1]][1]

The picture bellow shows the results and I was expecting outlier cluster to be clear compared the normal data.

Why so ?

Because for creating the anomalies I took the maximum value of **my_column** which was  9689. 

I am stuck here and I don’t know where to do from here, so I would appreciate some help.

The goal is that K mean to detected these added anomalies.


  [1]: https://i.sstatic.net/LHxX3.png