Using datatypes

There are several approaches to impose the datatype. For instance, if we want all entries of an already created array to be 32-bit floating point values, we may cast it as follows:

>>> import scipy.misc
>>> img=scipy.misc.lena().astype('float32')

We can also use an optional argument, dtype through the command:

>>> import numpy
>>> scores = numpy.array([101,103,84], dtype='float32')
>>> scores

The output is shown as follows:

array([ 101.,  103.,   84.], dtype=float32)

This can be simplified even further with a third clever method (although this practice offers code that are not so easy to interpret):

>>> scores = numpy.float32([101,103,84])
>>> scores

The output is shown as follows:

array([ 101.,  103.,   84.], dtype=float32)

The choice of datatypes for NumPy arrays is very flexible; we may choose the basic Python types (including bool, dict, list, set, tuple, str, and unicode), although for numerical...

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Learning SciPy for Numerical and Scientific Computing Second Edition - Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition

Overview of this book

Using datatypes

Learning SciPy for Numerical and Scientific Computing Second Edition - Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition

Overview of this book

Using datatypes

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