Saxons in Britain.

Gwynedd Blog

Saxons  along with Angles, Frisians and Jutes, invaded or migrated to the island of Great Britain (Britannia) around the time of the collapse of Roman authority in the west. Saxon raiders had been harassing the eastern and southern shores of Britannia for centuries before, prompting the construction of a string of coastal forts called the Litora Saxonica or Saxon Shore, and many Saxons and other folk had been permitted to settle in these areas as farmers long before the end of Roman rule in Britannia.

According to tradition, the Saxons (and other tribes) first entered Britain en masse as part of a deal to protect the Britons from the incursions of the Picts, Gaels and others. The story as reported in such sources as the Historia Brittonum and Gildas indicates that the British king Vortigern allowed the Germanic warlords, later named as Hengist and Horsa by Bede, to settle their people…

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The Deadliest Blogger: Military History Page


Unique among the territories of the Western Roman Empire in the 5th century, Britain succeeded in holding back and even reversing the tide of Germanic conquest for nearly two centuries. This was an age of heroes… It was the Age of Arthur! 

This is the first part in a multi-part examination of Britain, in the 5th though the mid-6th Century A.D. It is a fascinating period, with the Classical Age of Greece and Rome giving way to the Germanic “Dark Ages”. The heroes of this struggle, and the “barbarian” warlords who opposed them, are the subject of this discussion.

If he indeed existed (and it is the opinion of this author that he did) Arthur was a warlord who successfully led the British resistance to the Saxon threat. He lived in the late 5th century, and ruled Britain into the early 6th century. Using historical analysis of the disparate chronicles…

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Why is Python a language of choice for data scientists?

Why is Python a language of choice for data scientists? by Jeff Hammerbacher

Answer by Jeff Hammerbacher:

Python is an interpreted, dynamically-typed language with a precise and efficient syntax. Python has a good REPL and new modules can be explored from the REPL with dir() and docstrings. That's one reason to prefer Python over C, C++, or Java.

The Python community invested in the mid-1990s in Numeric, an "extension to Python to support numeric analysis as naturally as [M]atlab does" [1]. Numeric later evolved into NumPy [2]. Several years later, the plotting functionality from Matlab was ported to Python with matplotlib [3]. Libraries for scientific computing were built around NumPy and matplotlib and bundled into the SciPy package [4], which was commercially supported by Enthought [5]. Python's support for Matlab-like array manipulation and plotting is a major reason to prefer it over Perl and Ruby.

Today, the most popular alternatives to Python for data scientists are R, Matlab/Octave, and Mathematica/Sage. In addition to the work mentioned above to port features from Matlab into Python, recent work has ported several popular features from R and Mathematica into Python.

From R, the data frame and associated manipulations (from the plyr and reshape packages) have been implemented by the pandas library [6]. The scikit-learn project [7] presents a common interface to many machine learning algorithms, similar to the caret package in R.

From Mathematica/Sage, the concept of a "notebook" has been implemented with IPython notebooks [8].

From my personal perspective, Python is still lacking in a few important areas.

  1. The first is the more cumbersome syntax for array manipulations and formula specification in Python. The Matlab/Octave syntax for array manipulation is still preferred (that's why it's used in the Stanford ML class, for example), and the R syntax for formula specification is quite nice.
  2. The second is a Python equivalent to ggplot2 for static graphics and D3 for interactive graphics. The matplotlib library is hard to install, hard to use, and does not facilitate building interactive graphics for the web.
  3. The third is the scalability of NumPy and pandas when working with large data sets. The company Continuum [9] is working to address this problem, but they're a long way from producing something coherent and usable.
  4. The fourth is the lack of an embedded, declarative language for data manipulation, similar to the LINQ project. Pandas is useful as a low-level data manipulation toolkit, but tracking down the custom Pandas syntax for complex operations can be frustrating.
  5. The fifth is an IDE for data scientists of similar quality to R Studio.


Why is Python a language of choice for data scientists?