Structured arrays or record arrays are useful when you perform computations, and at the same time you could keep closely related data together. For example, when you process incident data and each incident contains geographic coordinates and the occurrence time, while you calculate the final result, you can easily find the associated geographic locations and timepoint for further visualization. NumPy also provides powerful capabilities to create arrays of records, as multiple data types live in one NumPy array. However, one principle in NumPy that still needs to be honored is that the data type in each field (you can think of this as a column in the records) needs to be homogeneous. Here are some simple examples that show you how it works:
In [20]: x = np.empty((2,), dtype = ('i4,f4,a10'))
In [21]: x[:] = [(1,0.5, 'NumPy'), (10,-0.5, 'Essential')]
In [22]: x
Out[22]:
array([(1, 0.5, 'NumPy'), (10, -0.5, 'Essential')],
dtype=[('f0', '<...
