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USKaushik Ghose
 

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    1. POHandling heterogenous data and large 'binary' data
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    2. POPandas DataFrame + object type + HDF + PyTables 'table'
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      1. POHow do I get the components for LDA in scikit-learn?
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      1. POException thrown in multiprocessing Pool not detected
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      1. POImage registration using python and cross-correlation
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    1. CO@Jeff Hmm, I had a funky problem with concurrent reads on an hdf5 file that I created with h5py (No writes). I was using Multiprocessing and ran into an intermittent problem where I would occasionally read garbage data. I had this regardless of whether I used a global file pointer or opened the file afresh in each thread. I had a devil of a time replicating the problem because it was so intermittent. Have you ever run into this?
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      1. POInsert or replace like operation for Pandas HDFStore (table)
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    2. CO@Jeff Thanks! In my use case I have a bunch of raw data files (sessions) that I ETL into a larger store. Pandas + Pytables does great, except when I have to go back to a previous session and reprocess it for some reason. In such a case I can not append but must first destroy the old entries. I found that tricky with Pandas. Also, I ran into some grief when I tried concurrent reads with an HDF5 store which I think is not an issue with sqlite
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      1. POInsert or replace like operation for Pandas HDFStore (table)
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    3. CO@TomAugspurger Thanks for the pointer! After reading your link I'm thinking that it may be better to store the actual data in a database and load relevant portions into Pandas. The more I look into it the more it seems Pandas is not that great with data that keeps changing but is awesome with static data. Is that correct?
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      1. POInsert or replace like operation for Pandas HDFStore (table)
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