Hands-On Artificial Intelligence for IoT

Using HDF5 with PyTables

Let's first create an HDF5 file from the numeric data we have in the temp.csv file with the following steps:

1. Get the numeric data:

import numpy as nparr = np.loadtxt('temp.csv', skiprows=1, usecols=(2,3), delimiter=',')

2. Open the HDF5 file:

import tablesh5filename = 'pytable_demo.hdf5'with tables.open_file(h5filename,mode='w') as h5file:

3. Get the root node:

    root = h5file.root

4. Create a group with create_group() or a dataset with create_array(), and repeat this until all the data is stored:

    h5file.create_array(root,'global_power',arr)

5. Close the file:

    h5file.close()

Let's read the file and print the dataset to make sure it is properly written:

with tables.open_file(h5filename,mode='r') as h5file: root = ...