input: Write a function to create a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. If the given dataset is empty, return an empty list of recommendations. The function should use the NearestNeighbors algorithm from sklearn.neighbors to find similar products.
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def product_recommendation(data):
      if data.empty:
          return []
      # 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 indices
  
  assert product_recommendation(pd.DataFrame([[1, 0, 3], [0, 1, 2], [3, 2, 1]])) == [[0, 1, 2], [1, 0, 2], [2, 1, 0]]
  assert product_recommendation(pd.DataFrame([[1, 0], [0, 1], [1, 1]])) == [[0, 2, 1], [1, 2, 0], [2, 0, 1]]
  assert product_recommendation(pd.DataFrame()) == []
output: def product_recommendation(data):
      if data.empty:
          return []
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      return indices