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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. You can use and chain multiple types of filter expressions without several loops in order to greatly simplify the filtering of objects.

Use through one of the list-type extending classes:

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

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

QueryableListMixed - QueryableList which can contain dict-like items or object-like item.

This is somewhat slower than using QueryableListObjs or QueryableListDicts directly, but use it if you need to mix, or need to support either type.

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.

The QueryableList types support all the operations of a list, and return the same QueryableList types so you can perform chaining. QueryableList also supports subtraction, whereas normal lists do not.

Items filtered do not need to be of the same type. If you filter on a field and it is not present on a member, the value of that field is assumed None (null) for comparison purposes.

You specify the filter operations by passing arguments of $fieldName__$operation.

Example: 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=2

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’}])

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