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[[!meta title="Python"]]
## Learning Python
### Which version to start? 2.x or 3.x?
Short answer: start learning 3.x and, if needed, check the differences with 2.x.
From [Should I use Python 2 or Python 3 for my development activity?](Should I use Python 2 or Python 3 for my development activity?):
Besides, several aspects of the core language (such as print and exec being
statements, integers using floor division) have been adjusted to be easier for
newcomers to learn and to be more consistent with the rest of the language, and
old cruft has been removed (for example, all classes are now new-style,
"range()" returns a memory efficient iterable, not a list as in 2.x).
[...]
In particular, instructors introducing Python to new programmers should
consider teaching Python 3 first and then introducing the differences in Python
2 afterwards (if necessary), since Python 3 eliminates many quirks that can
unnecessarily trip up beginning programmers trying to learn Python 2.
Also:
* [Python Future: Easy, clean, reliable Python 2/3 compatibility](http://python-future.org/).
* [Should I learn Python 2 or 3?](https://www.dataquest.io/blog/python-2-or-3/).
### 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.
So remember that when [copying](https://stackoverflow.com/questions/2612802/how-to-clone-or-copy-a-list#2612815)
or referencing a list.
Also, [take care with handling mutables as arguments and as default arguments](http://www.thedigitalcatonline.com/blog/2015/02/11/default-arguments-in-python/),
also explained [here](https://docs.python.org/3/tutorial/controlflow.html#default-argument-values) and [here](https://docs.python-guide.org/writing/gotchas/)
(common gotchas).
### Threads
From [GlobalInterpreterLock](https://wiki.python.org/moin/GlobalInterpreterLock):
In CPython, the global interpreter lock, or GIL, is a mutex that protects
access to Python objects, preventing multiple threads from executing Python
bytecodes at once. This lock is necessary mainly because CPython's memory
management is not thread-safe. (However, since the GIL exists, other features
have grown to depend on the guarantees that it enforces.)
[...]
The GIL is controversial because it prevents multithreaded CPython programs
from taking full advantage of multiprocessor systems in certain situations.
Note that potentially blocking or long-running operations, such as I/O, image
processing, and NumPy number crunching, happen outside the GIL. Therefore it is
only in multithreaded programs that spend a lot of time inside the GIL,
interpreting CPython bytecode, that the GIL becomes a bottleneck.
From: [Thread State and the Global Interpreter Lock](https://docs.python.org/3/c-api/init.html#thread-state-and-the-global-interpreter-lock):
When threads are created using the dedicated Python APIs (such as the threading
module), a thread state is automatically associated to them and the code showed
above is therefore correct. However, when threads are created from C (for
example by a third-party library with its own thread management), they don’t
hold the GIL, nor is there a thread state structure for them.
### Nice stuff
* [Verbose Regular Expressions](http://www.diveintopython3.net/regular-expressions.html#verbosere).
## Implementations
* [MicroPython - Python for microcontrollers](http://micropython.org/) ([compiling](https://github.com/micropython/micropython/wiki/Getting-Started).
## Libraries and applications
* QGIS.
* [SciPy.org — SciPy.org](https://www.scipy.org/) ([package](https://packages.debian.org/stable/python-scipy)).
## Frameworks
* [Welcome | Flask (A Python Microframework)](http://flask.pocoo.org/) ([package](https://packages.debian.org/stretch/python-flask)).
* Async: [asyncio](https://docs.python.org/3/library/asyncio.html), Twisted and Tornado.
* [Anaconda Data Science Platform](https://www.anaconda.com/).
## IDEs
* [PyCharm](https://www.jetbrains.com/pycharm/).
## Misc
* [Indentation](https://www.python.org/dev/peps/pep-0008/#indentation): Use 4 spaces per indentation level.
## 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)).
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