Examples & Snippets

glom can do a lot of things, in the right hands. This doc makes those hands yours, through sample code of useful building blocks and common glom tasks.


All samples below assume from glom import glom, T, Call and any other dependencies.

Reversing a Target

Here are a couple ways to reverse the current target. The first uses basic Python builtins, the second uses the T object.

glom([1, 2, 3], (reversed, list))
glom([1, 2, 3], T[::-1])

Iteration Result as Tuple

The default glom iteration specifier returns a list, but it’s easy to turn that list into a tuple. The following returns a tuple of absolute-valued integers:

glom([-1, 2, -3], ([abs], tuple))

Data-Driven Assignment

glom’s dict specifier interprets the keys as constants. A different technique is required if the dict keys are part of the target data rather than spec.

glom({1:2, 2:3}, Call(dict, args=(T.items(),)))
glom({1:2, 2:3}, lambda t: dict(t.items()))
glom({1:2, 2:3}, dict)

Construct Instance

A common use case is to construct an instance. In the most basic case, the default behavior on callable will suffice.

The following converts a list of ints to a list of decimal.Decimal objects.

glom([1, 2, 3], [Decimal])

If additional arguments are required, Call or lambda are good options.

This converts a list to a collection.deque, while specifying a max size of 10.

glom([1, 2, 3], Call(deque, args=[T, 10]))
glom([1, 2, 3], lambda t: deque(t, 10))

Filtered Iteration

Sometimes in addition to stepping through an iterable, you’d like to omit some of the items from the result set all together. Here are two ways to filter the odd numbers from a list.

glom([1, 2, 3, 4, 5, 6], lambda t: [i for i in t if i % 2])
glom([1, 2, 3, 4, 5, 6], [lambda i: i if i % 2 else SKIP])

The second approach demonstrates the use of glom.SKIP to back out of an execution.

This can also be combined with Coalesce to filter items which are missing sub-attributes.

Here is an example of extracting the primary email from a group of contacts, skipping where the email is empty string, None, or the attribute is missing.

glom(contacts, [Coalesce('primary_email.email', skip=('', None), default=SKIP)])

Preserve Type

The iteration specifier will walk lists and tuples. In some cases it would be convenient to preserve the target type in the result type.

This glomspec iterates over a tuple or list, adding one to each element, and uses T to return a tuple or list depending on the target input’s type.

glom((1, 2, 3), (
        "type": type,
        "result": [lambda v: v + 1]  # arbitrary operation
    }, T['type'](T['result'])))

This demonstrates an advanced technique – just as a tuple can be used to process sub-specs “in series”, a dict can be used to store intermediate results while processing sub-specs “in parallel” so they can then be recombined later on.

Automatic Django ORM type handling

In day-to-day Django ORM usage, Managers and QuerySets are everywhere. They work great with glom, too, but they work even better when you don’t have to call .all() all the time. Enable automatic iteration using the following register() technique:

import glom
import django.db.models

glom.register(django.db.models.Manager, iterate=lambda m: m.all())
glom.register(django.db.models.QuerySet, iterate=lambda qs: qs.all())

Call this in settings or somewhere similarly early in your application setup for the best results.

Filter Iterable

An iteration specifier can filter items out by using SKIP as the default of a Check object.

glom(['cat', 1, 'dog', 2], [Check(types=str, default=SKIP)])
# ['cat', 'dog']

You can also truncate the list at the first failing check by using STOP.

Lisp-style If Extension

Any class with a glomit method will be treated as a spec by glom. As an example, here is a lisp-style If expression custom spec type:

class If(object):
    def __init__(self, cond, if_, else_=None):
        self.cond, self.if_, self.else_ = cond, if_, else_

    def glomit(self, target, scope):
        g = lambda spec: scope[glom](target, spec, scope)
        if g(self.cond):
            return g(self.if_)
        elif self.else_:
            return g(self.else_)
            return None

glom(1, If(bool, {'yes': T}, {'no': T}))
# {'yes': 1}
glom(0, If(bool, {'yes': T}, {'no': T}))
# {'no': 0}

Parellel Evaluation of Sub-Specs

This is another example of a simple glom extension. Sometimes it is convenient to execute multiple glom-specs in parallel against a target, and get a sequence of their results.

class Seq(object):
    def __init__(self, *subspecs):
        self.subspecs = subspecs

    def glomit(self, target, scope):
        return [scope[glom](target, spec, scope) for spec in self.subspecs]

glom('1', Seq(float, int))
# [1.0, 1]

Without this extension, the simplest way to achieve the same result is with a dict:

glom('1', ({1: float, 2: int}, T.values()))

Clamp Values

A common numerical operation is to clamp values – if they are above or below a certain value, assign them to that value.

Using a pattern-matching glom idiom, this can be implemented simply:

glom(range(10), [(M < 7) | Val(7)])
# [0, 1, 2, 3, 4, 5, 6, 7, 7, 7]

What if you want to drop rather than clamp out-of-range values?

glom(range(10), [(M < 7) | Val(SKIP)])
# [0, 1, 2, 3, 4, 5, 6]

Transform Tree

With an arbitrary depth tree, Ref can be used to express a recursive spec.

For example, this etree2dicts spec will recursively walk an ElementTree instance and transform it from nested objects to nested dicts.

etree2dicts = Ref('ElementTree',
    {"tag": "tag", "text": "text", "attrib": "attrib", "children": (iter, [Ref('ElementTree')])})

Alternatively, say we only wanted to generate tuples of tag and children:

etree2tuples = Fill(Ref('ElementTree', (T.tag, Iter(Ref('ElementTree')).all())))

(Note also the use of Fill mode to easily construct a tuple.)

    <title>the title</title>
  <body id="the-body">
    <p>A paragraph</p>

Will translate to the following tuples:

>>> etree = ElementTree.fromstring(html_text)
>>> glom(etree, etree2tuples)
('html', [('head', [('title', [])]), ('body', [('p', [])])])

Fix Up Strings in Parsed JSON

Tree-walking with Ref() combines powerfully with pattern matching from Match().

In this case, consider that we want to transform parsed JSON recursively, such that all unicodes are converted to native strings.

            dict: {Ref('json'): Ref('json')},
            list: [Ref('json')],
            type(u''): Auto(str),
            object: T}))

Match() above splits the Ref() evaluation into 4 cases:

  • on dict, use Ref() to recurse for all keys and values
  • on list, use Ref() to recurse on each item
  • on text objects (type(u'')) – py3 str or py2 unicode – transform the target with str
  • for all other values (object), pass them through

As motivation for why this might come up: attributes, class names, function names, and identifiers must be the native string type for a given Python, i.e., bytestrings in Python 2 and unicode in Python 3.

Store and Retrieve Current Target

The A scope assignment helper makes it extremely convenient to hold on to the current target and then reset it.

The (A.t, …, S.t) “sandwich” is a convenient idiom for these cases.

For example, we could use this to update a dict:

glom({}, (A.t, T.update({1: 1}), S.t))