Ordered collections inspired by Enum

pip install enumap==1.5.0


Enumap: ordered data kept orderly

Enumap is an Enum that helps you manage named, ordered values in a strict but convenient way. Enumap isn't yet another collection, it's a store of keys that creates familiar ordered collections in a more expressive and less error prone way.

Make a specification for your data

Make a spec for your data with a simple, declarative Enum:

>>> from enumap import Enumap
>>> class Pie(Enumap):
...    rhubarb = "tart"
...    cherry = "sweet"
...    mud = "savory"

Or use the equivalent functional style:

>>> Pie = Enumap("Pie", "rhubarb cherry mud")

Easily create ordered collections from your data spec

Enumap.map and Enumap.tuple make familiar, reliable OrderedDicts and namedtuples with the same fields and ordering you used in your data spec.

>>> Pie.map(10, 23, mud=1)  # args and/or kwargs
OrderedDict([('rhubarb', 10), ('cherry', 23), ('mud', 1)])
>>> Pie.tuple(10, 23, 1000, cherry=1)  # override with kwargs
Pie_tuple(rhubarb=10, cherry=1, mud=1000)

Discover errors when your collections are created, not when they're used

KeyErrors keep you from going astray:

>>> Pie.tuple(rhubarb=1, cherry=1, mud=3, blueberry=30)
KeyError: "Pie requires keys ('rhubarb', 'cherry', 'mud'); got invalid keys {'blueberry'}"
>>> Pie.map(1, 1)
KeyError: "Pie requires keys ('rhubarb', 'cherry', 'mud'); missing keys {'mud'}"

With the Enumap data spec guiding you, you'll never let spelling errors seep deeper into your code:

>>> data = {"rhubarb": 10, "cherry": 23, "mud": 1}
>>> # elsewhere in your code
... new_data = dict(data, chery=0)  # 'cherry' is mispelled, but your dictionary doesn't care
>>> # even deeper into your code
... if not new_data["cherry"]:
...     # this block won't execute thanks to our spelling error earlier on!

Compose data safely with a single source for its order and naming

The Enumap spec acts like a tiny API for manipulating your data:

>>> data = Pie.tuple(10, 23, 1)
>>> new_data = Pie(*data, rhubarb=data.rhubarb * 2)  # customer wants more rhubarb
>>> bad_data = Pie(*data, chery=0)  # you'll know right away that you've mispelled 'cherry'
KeyError: "Pie requires keys ('rhubarb', 'cherry', 'mud'); got invalid keys {'chery'}"

Simple deserialization with type annotations

If you annotate your data fields with callable types, Enumap.tuple_casted and Enumap.map_casted will create deserialized collections from your data:

>>> import arrow  # convenient datetime library
>>> from enum import auto
>>> class CustomerOrder(Enumap):
...    index: int = "Order ID"
...    cost: Decimal = "Total pretax cost"
...    due_on: arrow.get = "Delivery date"
>>> serialized = "134,25014.99,2017-06-20"  # line from a CSV, for example
>>> CustomerOrder.tuple_casted(*serialized.split(","))
CustomerOrder_tuple(index=134, cost=Decimal('25014.99'), due_on=<Arrow [2017-06-20T00:00:00+00:00]>)

If you hate type annotations or if you prefer the functional Enum constructor, use Enumap.set_types:

>>> CustomerOrder.set_types(int, cost=Decimal, due_on=arrow.get)
>>> CustomerOrder.map_casted("22", "99.99", "2017-06-20")
OrderedDict([('index', 134), ('cost', Decimal('25014.99')), ...])

Sparse collections with the less strict SparseEnumap

Create collections with None (or other) defaults:

>>> from enumap import SparseEnumap
>>> SparsePie = SparseEnumap("SparsePie", "rhubarb cherry mud")
>>> SparsePie.tuple()
SparsePie_tuple(rhubarb=None, cherry=None, mud=None)
>>> SparsePie.tuple(2, cherry=1)
SparsePie_tuple(rhubarb=2, cherry=1, mud=None)

Use enumap.default() to declaratively specify defaults for missing values:

>>> class SparsePie(SparseEnumap):
...    rhubarb = default(33)
...    cherry = default(22)
...    mud = "this is not a default"
>>> SparsePie.tuple()
SparsePie_tuple(rhubarb=33, cherry=22, mud=None)

Alternatively, use SparseEnumap.set_defaults:

>>> SparsePie.set_defaults(cherry=0, mud=0)
>>> SparsePie.tuple(30)
SparsePie_tuple(rhubarb=30, cherry=0, mud=0)

Still, invalid keys are not allowed:

>>> SparsePie.tuple(cherry=1, rhubarb=1, mud=3, blueberry=30)
KeyError: "SparsePie has keys ('rhubarb', 'cherry', 'mud'); got invalid keys {'blueberry'}"


Enumap lets you define a set of keys or field names in your once in your code. This means:

  1. You get a single place to declaratively define an immutable set of keys or field names
  2. You can refer back to your keys and field names elsewhere without the uncertainty of using string literals or hard-to-debug global variables
  3. You can make containers from your keys without worrying that you've omitted or mispelled a key or field name

Why not just dictionaries with string keys?

String literals make fine dictionary keys for small projects.

data = dict(assembly="A1", reference="R3",
            name="resistor", subassembly=["U3", "W12"])

# later on
part_reference = data["reference"]

When a project grows beyond a certain size, you often see people keeping field names bound to global variables so that they can be imported in other modules. Usually the motivation for doing this is to improve clarity and ease refactoring.

PART_ASSEMBLY = "assembly"
PART_REFERENCE = "reference"
PART_SUBASSEMBLY = "subassembly"
PART_NAME = "name"

...and later they might use these global variables as dictionary keys:

assembly = data[PART_ASSEMBLY]
subassembly = data.get(PART_SUBASSEMBLY, [])

After a while it might be tempting to group key variables in an empty class:

class Part:
    assembly = "assembly"
    reference = "reference"

assembly = data[Part.assembly]

Now the code is more refactorable and less prone to error, but later on we may want a modified copy of our dictionary:

new_data = dict(data, asembly="A2")  # "assembly" is misspelled!

Now we've regressed to using plain strings and our code is prone to error once more. We could get around this by using advanced dictionary unpacking:

new_data = {**data, **{Part.assembly: "A2"}}

... but we've sacrificed readability for the sake of correctness.

How about plain namedtuples?

Namedtuples are great for making your code correct. They're ordered, immutable, and they insist on the field names they were born with.

Part = namedtuple("Part", "assembly reference subassembly name")
data = Part(assembly="A1", name="resistor", reference="R3", subassembly=[])

Looks great so far. We've got objects to pass around with immutable fields and convenient, expressive attribute access. Let's say we want to JSONify a Part. We'll want to convert it to a dict:

data_as_dict = data._as_dict()

So now we're left using a private namedtuple method just to get a dictionary out of our data! Say we're not done yet and we want to update a field in our dictionary before sending it out as JSON:

data_as_dict.update(asembly="A2")  # misspelled "assembly" error will go completely unnoticed!

Often we'll want to access our field names programmatically. Sadly, this also requires accessing a private namedtuple attribute. Say we're writing namedtuples to a CSV file:


🚽 Gross! Another private attribute!

How about regular ol' Enum members as keys?

Enum makes your code more debuggable. When you use Enum members as keys and parameters in your project, you never again have to wonder where literal strings like 'asembly' came from in a KeyError traceback. They're created in a clean, declarative fashion and they're immutable.

Part = Enum("Part", "assembly reference subassembly name")
part = {Part.assembly: "A1", Part.name: "resistor", ...}

At this point we have a part dictionary whose keys are easily debuggable Enum members. The problem is that you lose expressiveness when you create collections out of their members:

part.update({Part.assembly: "A2"})  # Enum members can't be **kwarg keys!
part[Part.assembly]  # lots of repetition and typing for something so simple

Our collection is no longer very REPL-friendly:

>>> part
{<Part.assembly: 0>: 'A2', <Part.subassembly: 1>: []...}

Also, you may eventually want to get your collections' keys back into plain string form (say, for JSONifying them):

jsonifyable_part = {key.name: value for key, value in part.items()}

... and nobody has time for that.

Enumap: expressive and strict

With Enumap, you get an immutable collection of keys from which you can create dicts and namedtuples. This approach gives you the best of both worlds: expressive, familiar data structures constructed by the same object that holds the keys, so incorrect keys will be discovered at the time your collections are made, not when they're used later on.

Part = Enumap("Part", "assembly reference subassembly name")
part_map = Part.map("A1", "R3", subassembly=[], name="resistor")
part = Part.tuple("A1", "R3", [], name="resistor")

If you use Part every time you want a new collection, you'll never let an invalid key pass silently through your code:

new_part_map = Part.map(*part_map.values(), assembly="A2")  # override assembly
new_part = Part.tuple(*part, assembly="A2")


PyPI version

Install with pip install enumap. Requires Python 3.6+.