README.md

Linqit!

Extends python's list builtin with fun, robust functionality - .NET's Language Integrated Queries (Linq) and more.
Write clean code with powerful syntax.

pip install linqit

Stop using loops, complex conditions, list comprehension and filters.
Doesn't it looks better?

from linqit import List

# Go ahead and fill the list with whatever you want... like a list of <Programmer> objects.
programmers = List()
Avi = type("Avi", (), {})
Entrepreneur = type("Entrepreneur", ('talented'), {})
elon_musk = Entrepreneur(talented=True)

# Then play:
last_hot_pizza_slice = (
    programmers.where(lambda e: e.experience > 15)
    .except_for(elon_musk)
    .of_type(Avi)
    .take(3)  # [<Avi>, <Avi>, <Avi>]
    .select(lambda avi: avi.lunch)  # [<Pizza>, <Pizza>, <Pizza>]
    .where(lambda p: p.is_hot() and p.origin != "Pizza Hut")
    .last()  # <Pizza>
    .slices.last()  # <PizzaSlice>
)

# What do you think?

We all use multiple aggregations in our code, while multiple filters/comprehensions are not pythonic at all.
The whole idea is is to use it for nested, multiple filters/modifications :).
Some of the methods might look ridiculous for a single calls, comparing to the regular python syntax.
Here are some use cases:

Methods:

all
any
concat
contains
distinct
except_for
first
get_by_attr
intersect
last
select
skip
take
where
of_type

Properties:

sum
min
max
avg
sorted

Deeper - Let's play with a list of people, a custom type.

import List

class Person():
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __repr__(self):
        return f'Person(name="{self.name}", age={self.age})')


# Creating a list of people
avi, bill, bob, harry = Person('Avi', 23), Person('Bill', 41), Person('Bob', 77), Person('Harry', 55)

people = List(avi, bill, bob, harry)

Use LINQ selections, write cleaner code

people = people.where(lambda p: p.age > 23) # [<Person name="Bill" age="41">, <Person name="Bob" age="77">, <Person name="Harry" age="55">]
people.first()                                              # <Person name="Bill" age="41">
people.last()                                               # <Person name="Harry" age="55">
people.any(lambda p: p.name.lower().startswith('b'))        # True
people.where(age=55)                         # [<Person name="Harry" age="55">]
people.skip(3).any()                                        # False
people.skip(2).first()                                      # <Person name="Harry" age="55">

# Isn't it better than "for", "if", "else", "filter", "map" and list comprehensions in the middle of your code?

More selections

new_kids_in_town = [Person('Chris', 18), Person('Danny', 16), Person('John', 17)]
people += new_kids_in_town # Also works: people = people.concat(new_kids_in_town)

teenagers = people.where(lambda p: 20 >= p.age >= 13)
danny = teenagers.first(lambda t: t.name == 'Danny')            # <Person name="Danny" age="16">
oldest_teen = teenagers.order_by(lambda t: t.age).last()                                  # <Person name="John" age="17">

Let's make python more dynamic

names = people.name                                             # ['Avi', 'Bill', 'Bob', 'Harry', 'Chris', 'John']
ages = people.age                                               # [23, 41, 77, 55, 18, 17]
teenagers_names = teenagers.name                                # ['Chris', 'Danny', 'John']
teenagers_names.take(2).except_for(lambda n: n == 'Danny')      # ['Chris']
teenagers.age.min                                               # 16
teenagers.age.avg                                               # 17
teenagers.age.max                                               # 18

Test Coverage

➜  linqit git:(master) ✗ coverage report                    
Name                  Stmts   Miss  Cover
-----------------------------------------
linqit/__init__.py        2      0   100%
linqit/linq_list.py     101     11    89%
tests/__init__.py         0      0   100%
tests/test_list.py      203      0   100%
-----------------------------------------
TOTAL                   306     11    96%