Download e-book for iPad: Beginning Python: From Novice to Professional (2nd Edition) by Magnus Lie Hetland

By Magnus Lie Hetland

Achieve a primary knowing of Python's syntax and contours with the second one version of starting Python, an up–to–date creation and sensible reference. protecting a big selection of Python–related programming themes, together with addressing language internals, database integration, community programming, and net providers, you'll be guided by means of sound improvement rules. Ten accompanying tasks will make sure you can get your palms soiled in no time.

Updated to mirror the newest in Python programming paradigms and several other of the main the most important positive aspects present in Python 3.0 (otherwise often called Python 3000), complicated issues, similar to extending Python and packaging/distributing Python functions, also are lined.

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Additional info for Beginning Python: From Novice to Professional (2nd Edition)

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This approach is suitable for very large data sets but is outside the scope of our discussion. info 21 Now we can explain the wide gray line in Figure 2-4: it is a KDE with a larger bandwidth. Using such a large bandwidth makes it impossible to resolve the individual data points, but it does highlight entire periods of greater or smaller frequency. Which choice of bandwidth is right for you depends on your purpose. A KDE constructed as just described is similar to a classical histogram, but it avoids two of the aforementioned problems.

Xn }. This formula can be evaluated for any point x along the x axis: n Dh (x; {xi }) = i=1 1 K h x − xi h All of this is straightforward and easy to implement in any computer language. , many different values of x). * * Yet another strategy starts with the realization that forming a KDE amounts to a convolution of the kernel function with the data set. You can now take the Fourier transform of both kernel and data set and make use of the Fourier convolution theorem. This approach is suitable for very large data sets but is outside the scope of our discussion.

Do many data points lie far away from the central group of points), or are most of the points—with the possible exception of individual outliers—confined to a restricted region? • If there are clusters, how many are there? Is there only one, or are there several? Approximately where are the clusters located, and how large are they—both in terms of spread and in terms of the number of data points belonging to each cluster? • Are the clusters possibly superimposed on some form of unstructured background, or does the entire data set consist only of the clustered data points?

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