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Temp. package to release model support in lookupy

Project description

Lookupy is a Python library that provides a Django QuerySet like interface to query (filter and select) data (list of dictionaries).

It actually started off as a library to parse and extract useful data out of HAR (HTTP Archive) files but along the way I felt that a generic library can be useful since I often find myself trying to get data out of JSON collections such as those obtained from facebook or github APIs. I choose to imitate the Django queryset API because of my familiarity with it.

I don’t use this library all the time but I do find it helpful when working with deeply nested json/dicts - the kind that Facebook, Github etc. APIs return. For everyday stuff I prefer Python’s built-in functional constructs such as map, filter, list comprehensions.


  • Python [tested for 2.7 and 3.2]

  • nose [optional, for running tests]

  • [optional, for test coverage]

  • Tox [optional, for building and testing on different versions of Python]


The simplest way to install this library is to use pip

$ pip install lookupy
Tip! Consider installing inside a


Quick start

Since this library is based on Django QuerySets, it would help to first understand how they work. In Django, QuerySets are used to construct SQL queries to fetch data from the database. Using the filter method of the QuerySet objects is equivalent to writing the WHERE clause in SQL.

Applying the same concept to simple collections of data (lists of dicts), lookupy can be used to extract a subset of the data depending upon some criteria that is specified using what is known as the “lookup parameters”.

But first, we need to construct a Collection object out of the data set as follows,

>>> from lookupy import Collection, Q
>>> data = [{'framework': 'Django', 'language': 'Python', 'type': 'full-stack'},
...         {'framework': 'Flask', 'language': 'Python', 'type': 'micro'},
...         {'framework': 'Rails', 'language': 'Ruby', 'type': 'full-stack'},
...         {'framework': 'Sinatra', 'language': 'Ruby', 'type': 'micro'},
...         {'framework': 'Zend', 'language': 'PHP', 'type': 'full-stack'},
...         {'framework': 'Slim', 'language': 'PHP', 'type': 'micro'}]
>>> c = Collection(data)

In order to filter some data out of collection, we call the filter method passing our lookup parameters to it.

>>> c.filter(framework__startswith='S')
<lookupy.lookupy.QuerySet object at 0xb740d40c>

>>> list(c.filter(framework__startswith='S'))
[{'framework': 'Sinatra', 'type': 'micro', 'language': 'Ruby'},
{'framework': 'Slim', 'type': 'micro', 'language': 'PHP'}]

A lookup parameter is basically like a conditional clause and is of the form <key>__<lookuptype>=<value> where <key> is a key in the dict and <lookuptype> is one of the predefined keywords that specify how to match the <value> with the actual value corresponding to the key in each dict. See list of lookup types

Multiple lookups passed as args are by default combined using the and logical operator (or and not are also supported as we will see in a bit)

>>> list(c.filter(framework__startswith='S', language__exact='Ruby'))
[{'framework': 'Sinatra', 'type': 'micro', 'language': 'Ruby'}]

For or and not, we can compose a complex lookup using Q objects and pass them as positional arguments along with our lookup parameters as keyword args. Not surprisingly, the bitwise and (&), or (|) and inverse (~) are overriden to act as logical and, or and not respectively (just the way it works in Django).

>>> list(c.filter(Q(language__exact='Python') | Q(language__exact='Ruby')))
[{'framework': 'Django', 'language': 'Python', 'type': 'full-stack'},
 {'framework': 'Flask', 'language': 'Python', 'type': 'micro'},
 {'framework': 'Rails', 'language': 'Ruby', 'type': 'full-stack'},
 {'framework': 'Sinatra', 'language': 'Ruby', 'type': 'micro'}]
>>> list(c.filter(~Q(language__startswith='R'), framework__endswith='go'))
[{'framework': 'Django', 'language': 'Python', 'type': 'full-stack'}]

Lookupy also supports having the result contain only selected fields by providing the select method on the QuerySet objects.

Calling the filter or select methods on a QuerySet returns another QuerySet so these calls can be chained together. Internally, filtering and selecting leverage Python’s generators for lazy evaluation. Also, QuerySet and Collection both implement the iterator protocol so nothing is evaluated until consumption.

>>> result = c.filter(Q(language__exact='Python') | Q(language__exact='Ruby')) \
              .filter(framework__istartswith='s')) \
>>> for item in result: # <-- this is where filtering will happen
...     print(item)
[{'framework': 'Sinatra'}]

For nested dicts, the key in the lookup parameters can be constructed using double underscores as request__status__exact=404. Finally, data can also be filtered by nested collection of key-value pairs using the same Q object.

>>> data = [{'a': 'python', 'b': {'p': 1, 'q': 2}, 'c': [{'name': 'version', 'value': '3.4'}, {'name': 'author', 'value': 'Guido van Rossum'}]},
...         {'a': 'erlang', 'b': {'p': 3, 'q': 4}, 'c': [{'name': 'version', 'value': 'R16B01'}, {'name': 'author', 'y': 'Joe Armstrong'}]}]
>>> c = Collection(data)
>>> list(c.filter(b__q__gte=4))
[{'a': 'erlang', 'c': [{'name': 'version', 'value': 'R16B01'}, {'y': 'Joe Armstrong', 'name': 'author'}], 'b': {'q': 4, 'p': 3}}]
>>> list(c.filter(c__filter=Q(name='version', value__contains='.')))
[{'a': 'python', 'c': [{'name': 'version', 'value': '3.4'}, {'name': 'author', 'value': 'Guido van Rossum'}], 'b': {'q': 2, 'p': 1}}]

In the last example, we used the Q object to filter the original dict by nested collection of key-value pairs i.e. we queried for only those languages for which the version string contains a dot (.). Note that this is different from filtering the nested collections themselves. To do that, we can easily construct Collection objects for the child collections.

See the examples subdirectory for more usage examples.

Supported lookup types

These are the currently supported lookup types,

  • exact exact equality (default)

  • neq inequality

  • contains containment

  • icontains insensitive containment

  • in membership

  • startswith string startswith

  • istartswith insensitive startswith

  • endswith string endswith

  • iendswith insensitive endswith

  • gt greater than

  • gte greater than or equal to

  • lt less than

  • lte less than or equal to

  • regex regular expression search

  • filter nested filter


  1. If a non-existent key is passed to select, then it will be included in the result with value None for all results.

  2. If a non-existent key is passed to filter, then the lookup will always fail. At first, this doesn’t seem consistent with the first point but it’s done to keep the overall behaviour predictable e.g. If a non-existent key is used with lt lookup with integer, say 2, as the val, then the lookup will always fail even though None < 2 == True in Python 2. Best is to just avoid such situations.

  3. Because of the way select works at the moment, if chained with filter it should be called only after it and not before (unless the keys used for lookup are also being selected.) I plan to fix this in later releases.

Running tests

$ make test

To conveniently test under all environments (Python 2.7 and 3.2), run,

$ tox


  • Measure performance for larger data sets

  • Implement CLI for JSON files


This library is provided as-is under the MIT License

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