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Python module to add support for ORM-style filtering to any list of items

Project description

Python module to add support for ORM-style filtering to any list of items

Use through one of the list-type extending classes:

QueryableListObjs - This assumes each item is an object [or implements __getattribute__].

QueryableListDicts - This assumes that each item is a dict [or implements __getitem__].

You can filter these objects by using the method “filterAnd” (or its alias, “filter”), or “filterOr”.

filterAnd returns a QueryableList where each item matches ALL of the provided criteria. filterOr returns a QueryableList where each item matches ANY of the provided criteria.

You specify the filter operations by passing arguments of $fieldName__$operation (e.x. results = objs.filter(name__ne=’Tim’) ), where “$fieldName” matches the name of an attribute/key and “$operation” is one of the following:

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” )

Full Documentation

Pydoc documentation can be found at: http://htmlpreview.github.io/?https://github.com/kata198/QueryableList/blob/master/doc/QueryableList.html?vers=1

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:nn’ %(data.filter(likes__containsAny=(‘puppies’, ‘bricks’)).filter(colour__ieq=’purple’),)) 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: [{‘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:

[{‘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:

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

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

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

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