izy

Python functions and classes that make it even easier! You will wonder why these are not already built-in in python! :)

You can skip README and follow (and run) its equivalent demo notebook.

The sooner you pip install izy, the less time you waste! Just 3 keystrokes to install and import :)

pip install izy

Now that you have it, here are the functionalities:

Sorting

Some operations like argsort need 1 or 2 lines to implement in python, but the code is not pythonically readable as it should be. Some other functions like topk are somehow hidden in the built-in modules, which we expose with ease!

Let us have a big list of numbers and give it an izy try:

from izy import *

mylist = [4.2, 10, -8, 0, 4, 33, 6, 97, 1, 6., 41, -6, 0.0]

>>> topk(mylist, 4)
[97, 41, 33, 10]

>>> topk(mylist, -3)     # as you would expect
[-8, -6, 0]

>>> argmin(mylist), argmax(mylist)
(2, 7)

>>> argsort(mylist)
[2, 11, 3, 12, 8, 4, 0, 6, 9, 1, 5, 10, 7]

>>> descending(mylist)   # I like it more than sorted(x, reverse=True)
[97, 41, 33, 10, 6, 6.0, 4.2, 4, 1, 0, 0.0, -6, -8]

>>> reorder(mylist, argsort(mylist))    # like numpy array indexing
[-8, -6, 0, 0.0, 1, 4, 4.2, 6, 6.0, 10, 33, 41, 97]

If you have a dict (or more precisely a Mapping), the arg* functions take keys as indices:

mydict = {'a': 1, 'b': 4, 'c': -1}

>>> argmin(mydict)
'c'

>>> reorder(mydict, argsort(mydict))    # as it should be
OrderedDict([('c', -1), ('a', 1), ('b', 4)])

If you liked what reorder() did to a dictionary, consider ordered() as an alternative to built-in sorted(). ordered() tries to return the sorted (ordered) variant of the input, while preserving its key properties. The behavior is as follows:

• Mapping-> OrderedDict which is a Mapping too.
• MutableSequence or MutableSet -> list (equivalent to sorted() in this case)
• immutable Sequence or Set -> tuple

>>> ordered(mydict)
OrderedDict([('c', -1), ('a', 1), ('b', 4)])

>>> ordered(frozenset({2, 4, -5}))
(-5, 2, 4)

Scorer

The Scorer is a dict subclass for scoring hashable items. It generalizes functionality of built-in Counter to floating-point numbers with full math operation support.

from izy import Scorer

s1 = Scorer({'a': 1, 'b': 2, 'c': 5})
s1['d'] = 3

>>> s1
Scorer({c: 5, d: 3, b: 2, a: 1})

s2 = Scorer('abc', [-2, 3, 4])

>>> s2
Scorer({c: 4, b: 3, a: -2})

Mathematical operators (+, -, *, /, //, %, **) are supported for both Scorer and scalar right-hand operands.

>>> s1 + s2
Scorer({c: 9, b: 5, d: 3, a: -1})

>>> s1 / 4
Scorer({c: 1.25, d: 0.75, b: 0.5, a: 0.25})

WARNING! To support partial update with math operations, we carry non-common items from the first operand unchanged, but those from the second operand are ignored.

Unary math operators (+, -) are available too.

>>> +s2             # special usage for useless uniary `+` (only keeps positive itmes)
Scorer({c: 4, b: 3})

>>> s1 + (+s2)      # not equivalent to s1 + s2
Scorer({c: 9, b: 5, d: 3, a: 1})

We also have abs(), round():

>>> abs(s2))
Scorer({c: 4, b: 3, a: 2})

>>> round(s2/4, 1)
Scorer({c: 1.0, b: 0.8, a: -0.5})

Logical operators &, | apply element-wise min/max and they are also applicable to scalars. As Scorer is not a set or even multiset (like Counter), we don't refer to these as intersection and union, but the functionalities are still similar.

>>> abs(s1 & s2)      # ~ min (drops non-common items)
Scorer({c: 4, a: 2, b: 2})

>>> s1 & 2            # drops items with score less than 2
Scorer({c: 5, d: 3, b: 2})

>>> s1 | s2           # ~ max
Scorer({c: 5, b: 3, d: 3, a: 1})

About the above warning, note that (s1 | (s1 & s2)) gives the subset of s1 which also exists in s2. You can use this to force math operations to return common items only.

And finally the ultimate goal of the Scorer is to sort its items according to scores and give us the best, topk (or worst, bottomk).

>>> s1.best()           
('c', 5)

>>> s1.topk(3)           # alias to `s1.best` but more readable when you specify the number of items
[('c', 5), ('d', 3), ('b', 2)]

>>> s1.topk(-2)         # negative k means bottom k
[('a', 1), ('b', 2)]

>>> s1.ascending()   # prefer this to `best` or `topk` with special values of n or k (None, 0, inf)
[('a', 1), ('b', 2), ('d', 3), ('c', 5)]

>>> s1.median()         # if scorer length is even, lower median is returned
('b', 2)

Decorators

Python functools is really interesting, but it really lacks some generally usefull decorators. There are some sippets scattered around and in PythonDecoratorLibrary, but I wanted to gather my favourites in a single package.

First, @returns() and @yields() make your function/generator return/yield namedtuples to more pythonically access your function output. It also looks like an easy documentation to me (about the meaning of what function returns and not just the type).

from izy import *

@returns(x='the input', plus_one='input + 1')
def myfunction(x):
    return x, x+1

@yields('a', 'a_doubled')
def mygenerator(x):
    for i in range(x):
        yield i, i*2

>>> myfunction(5)
myfunction_output(x=5, plus_one=6)

>>> myfunction(5).plus_one
6

>>> myfunction.__doc__
 Returns:
    x: the input
    plus_one: input + 1

@returns_time() calculates function runtime and returns it as a datetime.timedelta object. You can change time format to milis (int) or seconds (float). It returns a namedtuple which you can rename with using @returns.

@returns_time()
def timed(x):
    z = 0
    for i in range(x * 1000000):
        z += i

@returns('output', 'milis')
@returns_time(milis=True)
def timed_milis(x):
    z = 0
    for i in range(x * 1000000):
        z += i
    return 'done'

>>> timed(64)
timed_output(output=None, time=datetime.timedelta(seconds=4, microseconds=701631))

>>> timed_milis(32)
timed_milis_output(output='done', milis=2297)

@logs() does what it says at three stages: before calling, after returning and on exceptions. You can control log level for each stage, log to_file using default logger (named after the function) or pass your own logging.Logger.

import logging

root_logger = logging.getLogger()
root_logger.setLevel(logging.DEBUG)

@logs(before=logging.DEBUG, after=logging.INFO,
      to_file='logged.log', file_mode='a')
def logged(x, **kw):
    for i in range(1000000):
        x += i
  
>>> logged(1, key1=5, key2=7.2)
[2022-01-09 12:38:41][logged][DEBUG] - Function `logged` called with args: (1, key1=5, key2=7.2)
[2022-01-09 12:38:41][logged][INFO] - Function `logged` returned after 0:00:00.075534 with output: None

@fix_this() reminds you to fix something in a stronger way than a passive comment. It raises UserWarning at runtime and does this everytime :)

@fix_this('It always prints ValueError, errors should be raised!')
def please(x):
    print('ValueError')

This one @ignores() exceptions you define (and returns None). Who knows when and why you need this? :) You can stack it on top of @logs() to be aware of ignored exceptions.

@ignores(ValueError, IndexError)
@logs()
def ignorer(x):
    for i in range(x):
        raise ValueError

>>> ignorer(5)    # no error raises, only logging it
[2022-01-09 12:38:41][ignorer][DEBUG] - Function `ignorer` called with args: (5)
[2022-01-09 12:38:41][ignorer][ERROR] - ValueError() raised in `ignorer` after 0:00:00.003514!

Want more useful exception handler? @fallsback() falls back on specified exception to predefined return value or another callable. For multiple exceptions, stack it like this:

import math

@fallsback(ZeroDivisionError, float('inf'))
@fallsback(ValueError, lambda x: x**2)
def multifb(x):
    if x < 0:
        raise ValueError
    elif x == 0:
        raise ZeroDivisionError
    else:
      return 1 / math.sqrt(x)

>>> multifb(25)
0.2
>>> multifb(-5)   # falls back to callable
25
>>> multifb(0)     # falls back to value
inf

Bidict

If you already heard about (and used) the great bidict package, you can simply skip this one as this is merely a minimal bidict for those in a hurry :).

The Bidirectional dictionary is an invertible one-to-one mapping (a bijection). Obviously both sides should be valid dictionary keys. Uniqueness in either side is preserved by removing previous confilcting pairs, without any warnings.

from izy import Bidict

bd = Bidict({'a': 1, 'b': 2, 'c': 5})

bd = Bidict([('a', 1), ('b', 2), ('c', 5)])

bd = Bidict(a=1, b=2, c=5)  # nothing special, just like dict()

>>> bd              # all above are quivalent to
Bidict({'a': 1, 'b': 2, 'c': 5})

This is how you would access the mapping, in the direct way or reversed:

>>> bd.r     # or bd.reverse
{1: 'a', 2: 'b', 5: 'c'}
>>> bd['c']
5
>>> bd.r[5]
'c'

It is recommended to name the two directions of your Bidict to make your code more readable.

bd = Bidict('char', 'id')           # or Bidict('char-id')
bd.update({'a': 1, 'b': 2, 'c': 5})

>>> bd
Bidict({'a': 1, 'b': 2, 'c': 5})

bd = Bidict(char=['a', 'b', 'c'], id=[1, 2, 5])
>>> bd
Bidict({'a': 1, 'b': 2, 'c': 5})

>>> bd.id['c']
5
>>> bd.char[5]
'c'

>>> bd.char2id['c']   # more expressive access
5
>>> bd.id2char[5]
'c'

On evey update, the Bidict makes sure that all values in both directions remain unique. This is done by removing every existing pair which has conflict with the new one (overwritig).

bd.char[2] = 'c'  # two conflicting pairs ('b', 2), ('c', 5) are removed

>>> bd
Bidict({'a': 1, 'c': 2})

Not to mention that a Bidict and its reversed views (bd.r or other accessors) are still a dict.

>>> isinstance(bd, dict)
True

>>> for char, id in bd.r.items():
        print(char, id)
1 a
2 c

Regex

Here are some functions to help you create complex regular expressions. These functions are especially useful for some hard-to-remember and hard-to-read pattern syntaxes, like lookahead, lookbehind patterns.

p = make_regex(
   following('then '),      # lookbehind
   charset('a-z '), '+',
   followed_by(r' end')     # lookahead
)

>>> p.pattern
'(?<=then )[a-z ]+(?= end)'

>>> p.search('if condition then do something end; etc')
<re.Match object; span=(18, 30), match='do something'>

p = make_regex(
   any_of('ali', 'hossein', 'javad'),
   '\s+',
   grouped(any_of('goodarzi', 'sharafi'), name='family')
)

>>> p.pattern
'(?:hossein|javad|ali)\s+(?P<family>(?:goodarzi|sharafi))'

>>> match = p.search('his name is hossein sharafi.')
>>> match
<re.Match object; span=(12, 27), match='hossein sharafi'>

>>> match.group('family')
'sharafi'

Head

This simple function head() gives you a brief insight about the structure and contents of a possible large and nested python data object. The key use case is when you load a large .json file for the first time in a jupyter notebook.

x = [
   (1, 2, 3, 4),
   {1: range(3), 2: range(2, 40, 2), 5: 6, 7: 8},
   range(1000),
   (7, 8, 9, 10),
   *range(10)
]

>>> head(x)
[(1, 2, 3), {1: (0, 1, 2), 2: (2, 4, 6), 5: 6, 7: 8}, (0, 1, 2)]

>>> head(x, n=2, ellipsis=True)
[(1, 2, ...), {1: (0, 1, ...), 2: (2, 4, ...), ...: ...}, ...]

>>> print(phead(x))
[ (1, 2, 3, ...),
  {1: (0, 1, 2), 2: (2, 4, 6, ...), 5: 6, ...: ...},
  (0, 1, 2, ...),
  ...]

>>> hprint(x, n=5, width=32, indent=4)
[   (1, 2, 3, 4),
    {   1: (0, 1, 2),
        2: (   2,
               4,
               6,
               8,
               10,
               ...),
        5: 6,
        7: 8},
    (0, 1, 2, 3, 4, ...),
    (7, 8, 9, 10),
    0,
    ...]

Table

If you have a small tabular data and you don't want to install numpy, pandas just to handle it, here is the thing for you. The Table is a simple pure-python container for tabular data:

# create table using dict of lists (columns)
data = {
   'a': [3, 5, 21638540, 'very-loong-value'],
   'b': [16, 8, 0, 1],
   'c': [0, 5, 6, None],
   }
table = Table(data)

>>> print(table.draw())
a               b    c      
============================
3               16   0      
5               8    5      
21638540        0    6      
very-loong-va.. 1    None

# ... or a list of dicts (rows)
data = [
   {'a': 3, 'b': 16, 'c': 0},
   {'a': 5, 'b': 8, 'c': 5},
   {'a': 21638540, 'b': 0, 'c': 6},
   {'b': 1, 'a': 'very-loong-value'},
   ]
table = Table(data)

>>> print(table.draw())
a               b    c      
============================
3               16   0      
5               8    5      
21638540        0    6      
very-loong-va.. 1    None

You can easily access columns, rows, or a slice of the table:

>>> table['b']      # access table columns (by str key)
[16, 8, 0, 1]

>>> table[2]        # access table rows (by int key)
{'a': 21638540, 'b': 0, 'c': 6}

>>> table[1:3]      # or get a slice of table
a          b   c   
===================
5          8   5   
21638540   0   6

Concatenate tables:

>>> table[:2] + table[2:]
a               b    c      
============================
3               16   0      
5               8    5      
21638540        0    6      
very-loong-va.. 1    None

And manipulate table data:

# add/change a column
>>> table['d'] = [0] * len(table)
>>> table
a               b    c      d   
================================
3               16   0      0   
5               8    5      0   
21638540        0    6      0   
very-loong-va.. 1    None   0

# manipulate table rows
>>> table[1] = {'a': 5, 'b': 55, 'c': 555}
>>> table.append({'b': 66, 'c': 666})
>>> table
a               b    c      d      
===================================
3               16   0      0      
5               55   555    None   
21638540        0    6      0      
very-loong-va.. 1    None   0      
None            66   666    None

csv read/write is alse there:

>>> table.to_csv('output.tsv')
>>> Table.from_csv('output.tsv')
a               b    c      d      
===================================
3               16   0      0      
5               55   555    None   
21638540        0    6      0      
very-loong-va.. 1    None   0      
None            66   666    None

Path

Yet another path operations toolkit along with pathlib, path.py. This one is based on the standard pathlib and shares most of advantages of path.py. What makes it different is the convenient method-chaining api, and its file read/write methods.

d = Path('path/to').mkdir()
p = (d / 'file').with_suffix('.txt').touch()

>>> p.copy(d / 'copy.txt').name
'copy.txt'

>>> len(d.glob('*.txt'))
2
>>> d.parent.rmdir(recursive=True).exists()
False

FilePath is a Path which handles some file read/write operations which are not relevant to a Path.

f = FilePath('path/to', 'file.txt')

>>> f.mkdir().write_text('hello world!').read_text()
'hello world!'

>>> f.print({'x': 4, 'y':5}).aprint({'x': 6, 'y':7}).get_size()
36

>>> print(f.read_text())
{'x': 4, 'y': 5}
{'x': 6, 'y': 7}

>>> f.pickle({'x': 4, 'y': 5}).pickle({'x': 6, 'y': 7}, append=True).unpickle()
[{'x': 4, 'y': 5}, {'x': 6, 'y': 7}]

Read/write operations for json and csv (using izy.Table) are also available.