title: Computing Jaccard Similarity between DataFrame Columns with Different Lengths
body: <p>I have a dataframe with user_ids as columns and the ids of the movies they've liked as row values. Here's a snippet: </p>
  
  <pre><code>   15       30       50        93       100     113      1008    1028    
  0  3346.0  42779.0   1816.0  191319.0    138.0   183.0    171.0   283.0   
  1  1543.0      NaN    169.0    5319.0  34899.0   188.0  42782.0  1183.0   
  2  5942.0      NaN  30438.0  195514.0    169.0   172.0    187.0  5329.0   
  3  3249.0      NaN  32361.0     225.0     87.0   547.0   6710.0   283.0   
  4   794.0      NaN    187.0  195734.0   6297.0  8423.0   1289.0   222.0   
  </code></pre>
  
  <p>I'm trying to calculate the Jaccard Similarity between each column (i.e. between each user using the movies they've liked). Python gives the following error when I try to use the jaccard_similarity_score found in sklearn:</p>
  
  <pre><code>ValueError: continuous is not supported
  </code></pre>
  
  <p>Ideally, as a result, I would like to get a matrix with rows and columns of user_id's and the values as the similarity scores for each. </p>
  
  <p>How can I go about computing the jaccard similarities between these columns? I've tried to use a list of dictionaries with keys as user Ids and values as lists of movies, but it takes forever to compute. </p>