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The issue is not actually with the code, it is a compatibility issue, pinging the more modern servers are done by IPv6, while the code picks only IPv4 ICMP packets. An easy solution will be to restrict the ping to IPv4 by :

The last for loop should iterate over n, not ntrails (which happen to be the same value here) but otherwise the code and plots look correct (apart from a few minor issues such as that is takes 101 breaks to get 100 bins so your code should probably read bins = np.linspace(-2., 15., 101)).

Your plots could be improved a bit though. A good guiding principle is to use as little ink as necessary to communicate the point that you are trying to make. You are always trying to plot all the data, which ends up obscuring your plots. Also, you could benefit from paying more attention to colour. Colour should carry meaning, or not be used at all.

Here would be my suggestions:

From scikit-learn documentation:

The Perceptron is another simple classification algorithm suitable for large scale learning. By default:

• It does not require a learning rate.

• It is not regularized (penalized).

• It updates its model only on mistakes.

The last characteristic implies that the Perceptron is slightly faster to train than SGD with the hinge loss and that the resulting models are sparser.

And from here

Perceptron is a classification algorithm which shares the same underlying implementation with SGDClassifier. In fact, Perceptron() is equivalent to SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None)

So you should compare results with SGDClassifier by specifying the same parameters - loss function, learning rate, regularisation, random state, shuffle and etc.

Afaik, you must put cell magics at the beginning of a cell. However, %%csv is inside of your code block. Make sure that the section starting with %%csv is in a cell of its own. Otherwise, what is supposed to be a cell magic will be interpreted as a line magic.

You didn't call read, which is a method.

It looks like they changed the underlying file. The problem is not all the airports in airports_us have an IATA code assigned. This you see with

just want to leave an update: solved it by updating pyqt5 installation with easy_install. previously installed version (by sudo apt-get install python3.pyqt5) did not work

I don't believe what you are getting is due to an issue between python 3 and 2.7. Rather I believe the csv file that you are reading does not contain a header and as a result pandas is outputting as unnamed.

The indexed version might be cleaned up with slice objects like this: