QueryableList 1.2.0

Operations

• eq - Test equality ( = operator )

• ieq - Test equality, ignoring case (must be strings, or at least implement the .lower() method)

• ne - Test inequality ( != operator )

• ine - Test inequality, ignoring case (must be strings, or at least implement the .lower() method)

• lt - The item’s field value must be less than the provided value

• lte - The item’s field value must be less than or equal to the provided value

• gt - The item’s field value must be greater than the provided value

• gte - The item’s field value must be greater than or equal to the provided value

• isnull - Provided value must be True/False. If True, the item’s field value must be None, otherwise it must not be None.

• is - Test identity equality ( is operator )

• isnot - Test identity inequality ( is not operator )

• in - Test that the item’s field value is contained in the provided list of items

• notin - Test that the item’s field value is not contained in the provided list of items

• contains - Test that the item’s field value contains the provided value ( using “in” )

• notcontains - Test that the item’s field value does not contain the provided value ( using “not in” )

• containsAny - Test that the item’s field value contains any of the items in the provided list ( using “in” )

• notcontainsAny - Test that the item’s field value does not contain any of the items in the provided list ( using “not in” )

• splitcontains - Takes a tuple, (splitBy<str>, containsThis<str>). Use for a string that represents a list. The field will be split by the first, “splitBy”, param, and the result tested that it contains an item matching the second, “containsThis”, param. E.x. item__splitcontains=(’ ‘, ‘someValue’)

• splitnotcontains - Takes a tuple, (splitBy<str>, containsThis<str>). Use for a string that represents a list. The field will be split by the first, “splitBy”, param, and the result tested that it does not contain an item matching the second, “containsThis”, param.

• splitcontainsAny - Takes a tuple, (splitBy<str>, possibleMatches <list<str>>). Use for a string that represents a list. The field will be split by the first, “splitBy”, param, and the result tested that it contains any of the items in the provided list.

• splitnotcontainsAny - Takes a tuple, (splitBy<str>, possibleMatches <list<str>>). Use for a string that represents a list. The field will be split by the first, “splitBy”, param, and the result tested that it does not contains any of the items in the provided list.

Full Documentation

Pydoc documentation can be found at: http://htmlpreview.github.io/?https://github.com/kata198/QueryableList/blob/master/doc/QueryableList.html?vers=3

Example

Here is an example with some simple, silly data, doing some filters, followed by the results.

from QueryableList import QueryableListDicts, QueryableListObjs

import sys

class DataObj(object):

pass

class SampleDataObj(object):

def __init__(self, colour, age, name, likes):

self.colour = colour

self.age = age

self.name = name

self.likes = likes

def __str__(self):

return str(self.__dict__)

__repr__ = __str__

if __name__ == ‘__main__’:

#data = [{‘colour’: ‘purple’, ‘age’: 31, ‘name’: ‘Tim’, ‘likes’ : [‘puppies’, ‘rainbows’]}, {‘colour’: None, ‘age’: 19, ‘name’: ‘Joe’, ‘likes’ : [‘puppies’, ‘cars’]}, {‘colour’: ‘PURPLE’, ‘age’: 23, ‘name’: ‘Joe’, ‘likes’ : [‘cheese’, ‘books’]}]

data = [

SampleDataObj(colour=’purple’, age=31, name=’Tim’, likes=[‘puppies’, ‘rainbows’]),

SampleDataObj(colour=None, age=19, name=’Joe’, likes=[‘puppies’, ‘cars’]),

SampleDataObj(colour=’PURPLE’, age=23, name=’Joe’, likes=[‘cheese’, ‘books’]),

]

#data = QueryableListDicts(data)

data = QueryableListObjs(data)

sys.stdout.write(“Data: %snn” %(data,))

sys.stdout.write(‘People who are over 22 years old:n%snn’ %(data.filter(age__gt=22),))

sys.stdout.write(‘People who like puppies or bricks, and their favourite colour is purple:n%snn’ %(data.filter(likes__containsAny=(‘puppies’, ‘bricks’), colour__ieq=’purple’),))

sys.stdout.write(‘People who are at least 30 years old or like cheese:n%snn’ %(data.filterOr(likes__contains=’cheese’, age__gte=30),))

#import pdb; pdb.set_trace()

Results:

Data: QueryableListObjs([{‘colour’: ‘purple’, ‘likes’: [‘puppies’, ‘rainbows’], ‘age’: 31, ‘name’: ‘Tim’}, {‘colour’: None, ‘likes’: [‘puppies’, ‘cars’], ‘age’: 19, ‘name’: ‘Joe’}, {‘colour’: ‘PURPLE’, ‘likes’: [‘cheese’, ‘books’], ‘age’: 23, ‘name’: ‘Joe’}])

People who are over 22 years old:

QueryableListObjs([{‘colour’: ‘purple’, ‘likes’: [‘puppies’, ‘rainbows’], ‘age’: 31, ‘name’: ‘Tim’}, {‘colour’: ‘PURPLE’, ‘likes’: [‘cheese’, ‘books’], ‘age’: 23, ‘name’: ‘Joe’}])

People who like puppies or bricks, and their favourite colour is purple:

QueryableListObjs([{‘colour’: ‘purple’, ‘likes’: [‘puppies’, ‘rainbows’], ‘age’: 31, ‘name’: ‘Tim’}])

People who are at least 30 years old or like cheese:

QueryableListObjs([{‘colour’: ‘purple’, ‘likes’: [‘puppies’, ‘rainbows’], ‘age’: 31, ‘name’: ‘Tim’}, {‘colour’: ‘PURPLE’, ‘likes’: [‘cheese’, ‘books’], ‘age’: 23, ‘name’: ‘Joe’}])