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-[[!meta title="Python"]]
-
-## Learning Python
-
-### General
-
-* Everything is an object. Really? What about symbols like + - and =?
-* The `dir()` and `help()` functions are really useful.
-* Great idea: iteration protocol.
-* There are sequences and sum operations common for all types and specific type operations.
-
-### Iteration and optimization
-
-    In general, leading and trailing double underscores is the naming pattern
-    Python uses for implementation details. The names without the underscores in
-    this list are the callable methods on string objects.
-
-### Polymorphism
-
-Python encourages polymorphism:
-
-    This is related to the idea of polymorphism mentioned earlier, and it stems
-    from Python’s lack of type declarations. As you’ll learn, in Python, we code to
-    object interfaces (operations supported), not to types. That is, we care what
-    an object does, not what it is. Not caring about specific types means that code
-    is automatically applicable to many of them—any object with a compatible
-    interface will work, regardless of its specific type. Although type checking is
-    supported—and even required in some rare cases—you’ll see that it’s not usually
-    the “Pythonic” way of thinking. In fact, you’ll find that polymorphism is
-    probably the key idea behind using Python well.
-
-### Numeric Display Formats
-
-* [14. Floating Point Arithmetic: Issues and Limitations — Python 2.7.13 documentation](https://docs.python.org/2/tutorial/floatingpoint.html)
-* [What Every Computer Scientist Should Know About Floating-Point Arithmetic](https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html)
-* [Floating-point arithmetic - Wikipedia](https://en.wikipedia.org/wiki/Floating-point_arithmetic).
-
-    This floating-point limitation is especially apparent for values that cannot be
-    represented accurately given their limited number of bits in memory.
-
-    [...]
-
-    fractions and decimals both allow more intuitive and accurate results than
-    floating points sometimes can, in different ways—by using rational
-    representation and by limiting precision
-
-### Types
-
-    More formally, there are three major type (and operation) categories in Python
-    that have this generic nature:
-
-    Numbers (integer, floating-point, decimal, fraction, others)
-    Support addition, multiplication, etc.
-    Sequences (strings, lists, tuples)
-    Support indexing, slicing, concatenation, etc.
-    Mappings (dictionaries)
-    Support indexing by key, etc.
-
-    [...]
-
-    The major core types in Python break down as follows:
-
-    Immutables (numbers, strings, tuples, frozensets)
-    None of the object types in the immutable category support in-place changes,
-    though we can always run expressions to make new objects and assign their
-    results to variables as needed.
-
-    Mutables (lists, dictionaries, sets, bytearray)
-    Conversely, the mutable types can always be changed in place with operations
-    that do not create new objects. Although such objects can be copied, in-place
-    changes support direct modification.
-
-## Libraries and applications
-
-* QGIS.
-* [SciPy.org — SciPy.org](https://www.scipy.org/) ([package](https://packages.debian.org/stable/python-scipy)).
-
-## Test projects
-
-* [Arduino Blog » How close are we to doomsday? A clock is calculating it in real time](https://blog.arduino.cc/2013/03/27/how-close-are-we-to-doomsday-clock/) ([python code](https://github.com/tomschofield/Neurotic-Armageddon-Indicator/blob/master/NAI_SERVER/nai_scraper.py) to parse [Timeline from the Bulletin of the Atomic Scientists](http://thebulletin.org/timeline)).