I am reading dat开发者_JAVA技巧a from a .mat file using the Pytables module. After reading the data, I want to insert this data into the database using psycopg.
I have a rather large HDF5 file generated by PyTables that I a开发者_运维问答m attempting to read on a cluster. I am running into a problem with NumPy as I read in an individual chunk. Let\'s go with
I am trying to append a large dataset (>30Gb) to an existing pytables table.The table is N columns, and the dataset is N-1 columns; one column is calculated after I know the other N-1 columns.
I bought Kibot\'s stock data and it is enormous.I have about 125,000,000 rows to load (1000 stocks * 125k rows/stock [1-minute bar data since 2010-01-01], each stock in a CSV file whose fields are Dat
I have a dataset with 300+ columns in pytables, and I want to be able to choose different subsets easily.There doesn\'t seem to be a very elegant solution to this, or is there something I\'m missing?
I have a cell array in matlab columns = {\'MagX\', \'MagY\', \'MagZ\', ... \'AccelerationX\',\'AccelerationX\',\'AccelerationX\', ...
I\'m going to be running a large number of simulations producing a large amount of data that needs to be stored and accessed again later. Output data from my simulation program is written to text file
I have daily stock data as an HDF5 file created using PyTables. I would like to get a group of rows, process it as an array and then write it back to disk (update rows) using PyTables. I couldn\'t fig
It seems that the PyTable columns are alphabetically ordered when using both dictionary or class for schema definition for the call to createTable(). My need is to establish a specific order and then
ptrepack is almost what i want except that it only has the options to overwrite or ignore duplicate paths. the example below illustrates what i want to happen with the structures
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