input: Write a function to create a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read in data from a CSV file named 'product_data.csv', create an item-based collaborative filter using NearestNeighbors from sklearn, and make recommendations. Finally, it should print the recommendations for each product in the dataset.
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def product_recommender():
      # Read in data
      data = pd.read_csv('product_data.csv')
      
      # 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 product_recommender()  # This assert is for function execution, no return value to check.
output: 
  def product_recommender():
      # Read in data
      data = pd.read_csv('product_data.csv')
      
      # 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])
  