input: Write a function to perform K-means clustering on customer data and return the centroids.
  assert len(perform_kmeans_clustering('customer_data.csv', 3)) == 3
  assert isinstance(perform_kmeans_clustering('customer_data.csv', 3), np.ndarray)
  assert perform_kmeans_clustering('customer_data.csv', 3).shape == (3, 3)
output: import numpy as np
  import pandas as pd
  from sklearn.cluster import KMeans
  
  def perform_kmeans_clustering(filename, n_clusters):
      # Load the customer data from a CSV file
      data = pd.read_csv(filename)
      
      # Assume that the relevant features are all columns except the first one (ID or non-numeric data)
      features = data.iloc[:, 1:]
  
      # Perform K-means clustering
      kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(features)
      
      # Get the centroids
      centroids = kmeans.cluster_centers_
      
      return centroids