input: Write a function to create a recommendation system in Python using Pandas to recommend new products to customers based on past purchases.
  
  def create_recommendation_system(product_data_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      # Read in data
      data = pd.read_csv(product_data_path)
      
      # Create an item-based collaborative filter
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      # Make recommendations
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      # Return results as a list of recommendations
      recommendations = []
      for i in range(len(data)):
          recommendations.append('Recommendations for item {}:'.format(i) + str(indices[i]))
      
      return recommendations
  
  assert create_recommendation_system('product_data.csv')[0] == 'Recommendations for item 0: [0 1 2]'
  assert create_recommendation_system('product_data.csv')[1] == 'Recommendations for item 1: [1 0 2]'
  assert create_recommendation_system('product_data.csv')[2] == 'Recommendations for item 2: [2 1 0]'
output: def create_recommendation_system(product_data_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      data = pd.read_csv(product_data_path)
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      recommendations = []
      for i in range(len(data)):
          recommendations.append('Recommendations for item {}:'.format(i) + str(indices[i]))
      
      return recommendations