Difference between revisions of "Hdf5 table"
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PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. | PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. | ||
PyTables is built on top of the HDF5 library, using the Python language and the NumPy package. | PyTables is built on top of the HDF5 library, using the Python language and the NumPy package. | ||
| − | The following example shows how to store a table of 10 records : | + | The following example shows how to store a table of 10 records with 7 attributes : |
{|border="1" cellpadding="10" cellspacing="0" | {|border="1" cellpadding="10" cellspacing="0" | ||
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Revision as of 12:11, 17 December 2013
Storing table in HDF5
The HDF5 Table interface condenses the steps needed to create tables in HDF5. The datatype of the dataset that gets created is of type H5T_COMPOUND. The members of the table can have different datatypes.
Writting a table using Python (PyTables)
PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. PyTables is built on top of the HDF5 library, using the Python language and the NumPy package. The following example shows how to store a table of 10 records with 7 attributes :
| name | ADCcount | grid_i | grid_j | pressure | energy | idnumber |
|---|---|---|---|---|---|---|
| 16-character String | Unsigned short integer | 32-bit integer | 32-bit integer | float (single-precision) | double (double-precision) | Signed 64-bit integer |