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copied!<p>Numpy is incredibly flexible and powerful when it comes to views into arrays whilst minimising copies. For example:</p> <pre><code>import numpy a = numpy.random.randint(0, 10, size=10) b = numpy.a[3:10] </code></pre> <p>b is now a view of the original array that was created.</p> <p>Numpy arrays allow all manner of access directly to the data buffers, and can be trivially typecast. For example:</p> <pre><code>a = numpy.random.randint(0, 10, size=10) b = numpy.frombuffer(a.data, dtype='int8') </code></pre> <p>b is now view into the memory with the data all as 8-bit integers (the data itself remains unchanged, so that each 64-bit int now becomes 8 8-bit ints). These buffer objects (from a.data) are standard python buffer objects and so can be used in all the places that are defined to work with buffers.</p> <p>The same is true for multi-dimensional arrays. However, you have to bear in mind how the data lies in memory. For example:</p> <pre><code>a = numpy.random.randint(0, 10, size=(10, 10)) b = numpy.frombuffer(a[3,:].data, dtype='int8') </code></pre> <p>will work, but</p> <pre><code>b = numpy.frombuffer(a[:,3].data, dtype='int8') </code></pre> <p>returns an error about being unable to get single-segment buffer for discontiguous arrays. This problem is not obvious because simply allocating that same view to a variable using</p> <pre><code>b = a[:,3] </code></pre> <p>returns a perfectly adequate numpy array. However, it is not contiguous in memory as it's a view into the other array, which need not be (and in this case isn't) a view of contiguous memory. You can get info about the array using the flags attribute on an array:</p> <pre><code>a[:,3].flags </code></pre> <p>which returns (among other things) both C_CONTIGUOUS (C order, row major) and F_CONTIGUOUS (Fortran order, column major) as False, but </p> <pre><code>a[3,:].flags </code></pre> <p>returns them both as True (in 2D arrays, at most one of them can be true).</p>

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