Issue with using Sparse Data Frame in Mlxtend Apriori function I am running python 2.7 in anaconda and have installed mlxtend. Based on the latest version of mlxtend, the aprioir class supports sparse dataframe as its input. I have over 500k products that I want to run a market basket analysis on. 

I have created a onehot encoded sparse dataframe using a small dataset to test but I am running into df.to_coo() issue on the sparse data frame inside the mlextend apriori function.

Please find the code, the input data file and the errors I get here -
https://github.com/nshahHome/pycode

Click on the view code to see the files.

code = code2.py  ,  input data file= mbatest.txt   ,   errors =    code2-error.html (pdf version)  ,   condalist.txt

I expect the code to not throw errors and try to create frequent_itemsets. The set could be empy if there are no sets > min_support.
This question is now closed as it has been accepted as an enhancement needed by the developer.

https://github.com/rasbt/mlxtend/issues/501