input: Write a function to create a recommendation system in Python that uses Pandas to recommend new products to customers based on past purchases. The function should read a CSV file containing product data, train an item-based collaborative filter using the NearestNeighbors algorithm, and then print recommendations for each item in the dataset.
  
  
  def recommend_products(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      # Read in data
      data = pd.read_csv(file_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)
  
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])
  
  assert recommend_products('product_data.csv')  # Test case output not applicable for this function
  
output: def recommend_products(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      # Read in data
      data = pd.read_csv(file_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)
  
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])