Finding number of clusters in k means clustering automatically

I have a satellite image which i converted into a data frame.

enter image description here

I want to perform clustering but find the number of clusters in k means clustering automatically. Below is the python code that I have tried-

# imports
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from joblib import Parallel, delayed

def kMeansRes(scaled_data, k, alpha_k=0.02):
'''
Parameters 
----------
scaled_data: matrix 
scaled data. rows are samples and columns are features for clustering
k: int
current k for applying KMeans
alpha_k: float
manually tuned factor that gives penalty to the number of clusters
Returns 
-------
scaled_inertia: float
scaled inertia value for current k           
'''

inertia_o = np.square((scaled_data - np.mean(scaled_data)))
# fit k-means
kmeans = KMeans(n_clusters=k, random_state=0).fit(scaled_data)
scaled_inertia = kmeans.inertia_ / inertia_o + alpha_k * k
return scaled_inertia

def chooseBestKforKMeans(scaled_data, k_range):
ans = []
for k in k_range:
   scaled_inertia = kMeansRes(scaled_data, k)
   ans.append((k, scaled_inertia))
   results = pd.DataFrame(ans, columns = ['k','Scaled Inertia']).set_index('k')
   best_k = results.idxmin()[0]
   return best_k, results

kMeansRes(df, 4, alpha_k=0.02)
chooseBestKforKMeansParallel(df,range(0,10))

I am getting the below warning and error as- cannot convert float infinity to integer