Python notes - semi-sorted

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Syntaxish: syntax and language · changes and py2/3 · decorators · importing, modules, packages · iterable stuff · concurrency

IO: networking and web · filesystem

Data: Numpy, scipy · pandas, dask · struct, buffer, array, bytes, memoryview · Python database notes

Image, Visualization: PIL · Matplotlib, pylab · seaborn · bokeh · plotly

Tasky: Concurrency (threads, processes, more) · joblib · pty and pexpect

Stringy: strings, unicode, encodings · regexp · command line argument parsing · XML

date and time


speed, memory, debugging, profiling · Python extensions · semi-sorted

Setting the process name

It's OS-specific stuff, so there is no short portable way.

The easiest method is to install/use the setproctitle module - it aims to be portable and try its best on various platforms. Example use:

    import setproctitle
    setproctitle.setproctitle( os.path.basename(sys.argv[0]) )
except ImportError:

The above often means 'use the filesystem name that was used to run this' - but not always, so a hardcoded string can make sense.

Temporary files

array, deque

A list can be used as a sort of deque, like:

append(val)     # insert on right side 
pop()           # take from right side
insert(0,val)   # insert on left side
pop(0)          # take from left side

However, list is primarily efficient for stack-like use - list.pop(0) and list.insert(0, val) are O(n) operations,

...while those are O(1) operations on collections.deque (added in 2.4). deque also has appendleft(), extendleft(), and popleft(), and some others that can be convenient and/or more readable.

You may also wish to know about the queue nodule, a multi-producer, multi-consumer, thread-safe queue.

See also:

Some library notes

pytest notes

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, or tell me)

Pytest wants to find tests itself - this is an inversion of control thing, with the point is that no matter how complex tests get, pytest can do everything for us without us having to hook it in specifically.

What files does pytest pick up to be tested?

  • the filenames specified
  • if none specified:
filenames named like test_*.py or * on the directory tree under the current dir(verify)
You can control this discovery, see e.g.

How does pytest decide what to run as tests?

  • functions prefixed
    at module scope (verify)
  • Classes prefixed
    , and then functions prefixed
    inside them
...but only if that class does not have an __init__.
These classes are not intended to be classes with state (and it does not actually instantiate the class(verify)), just to group test functions and pollute the namespace less.
  • classes subclassed from unittest.TestCase (see unittest)
  • by marker, e.g. pytest -m slow picks up things decorated with @pytest.mark.slow
useful to define groups of tests

What does pytest actually consider success/failure?

Roughly: each test function will be a

  • success:
if it returns, AND
if all
s contained (if any) are successful
  • failure: fails on the first failing
  • failure: fails on the first exception

Expressing further things

  • testing that something throws and exception has a few alternatives, but using a context manager is probably the shortest form:
with pytest.raises(ValueError, match=r'.*BUG.*'):
    raise ValueError("BUG: tell programmer")
Another would be catchng it and invoking
except ZeroDivisionError as exc:

  • similarly, there is a pytest.warns to test that warnings happen
the context object variant is probably preferred here

More detailed testing can be done like

  • anything else that does asserts, like
unittest.assertEqual(a, b)
unittest.assertNotEqual(a, b)
unittest.assertIs(a, b)
unittest.assertIsNot(a, b)
unittest.assertIn(a, b)
unittest.assertNotIn(a, b)
unittest.assertIsInstance(a, b)
unittest.assertNotIsInstance(a, b)
...but most of those are shorter if you write the assert yourself.

Showing details

Pytest will try to pick up more to report for failed tests, e.g.:

comparing long strings: a context diff is shown
comparing long sequences: first failing indices
comparing dicts: different entries

There is more customization you can do - sometimes nice to get more actionable output from pytest runs.

On fixtures/mocking

Fixtures create reusable state/helpers for tests, and are great if you use the same data/objects.

For terminology, see Benchmarking,_performance_testing,_load_testing,_stress_testing,_etc.#Mocking.2C_monkey_patching.2C_fixtures

For what pytest helps with, see [1]



Win32 interface

pywin32, previously known as win32all, provides hooks into various parts of windows. Apparently with central module win32api. (see also its help file)

downloadable from sourceforge and with a homepage here.

Some code for this:


This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, or tell me)










Creating ZIP files (in-memory)

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, or tell me)

We can use in-memory data and StringIO objects here - so this is sometimes simpler than doing the same with tarfile.

You can add content with either:

  • ZipFile.write - takes filename it will open - useful when you just wanted more control over what files to add
  • ZipFile.writestr takes a filename/ZipInfo and a bytestring - the filesystem isn't touched, which is probably what you want when adding in-memory data to in-memory zip files.
# Just an example snippet
z = zipfile.ZipFile(zip_sio, "w", zipfile.ZIP_DEFLATED) # or another compression level
for filename,filedata in (('filename1.txt', 'foo'),
                          ('filename2.txt', 'bar')):
return zip_sio.getvalue()


There are a few implementations/modules, including:

  • fftpack: used by numpy.fft, scipy.fftpack

Speed-wise: FFTW is faster, numpy is slower, scipy is slower yet (not sure why the np/sp difference when they use the same code) Think a factor 2 or 3 (potentially), though small cases can be drowned in overhead anyway.

Also, FFTW planning matters.

It does matter how the coupling works - there for example are more and less direct (overhead-wise) ways of using FFTW.

TODO: figure out threading, MPI stuff.

See also:

Bytecode / resolve-related notes

  • .py - source text
  • .pyc - compiled bytecode
  • .pyo - compiled bytecode, optimized. Written when python is used with -O. The difference with pyc is currently usually negligible.
  • .pyd - a (windows) dll with some added conventions for importing
(and path-and-import wise, it acts exactly like the above, not as a linked library)
note: native code rather than bytecode

All of the above are searched for by python itself.

Python it will generate pyc or pyo

when they are imported them (not when the modules are run directly)
...with some exceptions, e.g. when importing from an egg, or zip file it will not alter those
...which means it can, for speed reasons, be preferable to distribute those with pyc/pyo files in them

There is some talk about changing these, see e.g. PEP 488

See also:



documentation generators

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, or tell me)

Further notes

!=None versus is not None


  • 99% of the time it makes no difference
  • ...but for the cases where it does, it's it's good practice to use
  • the reason is only true for singletons like None
and not necessarily for booleans, because you are probably used to having those coerced to bool;
= True
1 is not True


PEP8 says that comparisons to singletons should be done with
/ {{inlinecode|is not||, and not equality (
). The reason is roughly that a class is free to implement the equality operator (==) any way it wants, whereas
is defined by the language and will always do what you think.

Whenever a class doesn't redefine the equality operator, then == is often just as good.

Even when it does redefine it, it is still not necessarily a problem, due to what people usually use the equality comparison operator for.

But as general practice,
is cleaner.

One real-world example is numpy

Where you write

if ary==None:

What you probably wanted to check whether the variable has an array (or any object) assigned at all.

But numpy defines the == comparison so that you can e.g. compare an array to a scalar, evaluated element-wise, like

>>> numpy.array( [(1, 2), (2, 1)]  ) == 2
array([[False,  True],
       [ True, False]])
would almost always give an all-False matrix, because numpy won't even be able to store None unless you use dtype=object, which is often specifically avoided in number crunching.