vivodict 0.3.1
Auto-vivified arbitrarily-nested dictionary (`dict`) classes.
License: MIT license
Keywords: vivodict

========
vivodict
========


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        :target: https://vivodict.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status


This package provides a simple implementation of an `auto-vivified
<https://en.wikipedia.org/wiki/Autovivification>`_ Python ``dict``, i.e., a
dictionary where accessing a missing key doesn't raise the standard ``KeyError``
exception but instead implicitly creates and returns an empty auto-vivified
``dict`` under that key.


Features
--------

* Auto-vivified ``VivoDict`` class derived from the standard Python ``dict``
  class (no third-party dependencies).
* Auto-vivification of arbitrarily-nested ``dict`` objects.
* Convenience methods for ``flatten``, ``replace``, and ``apply`` operations.
* Free software: MIT license
* Documentation: https://vivodict.readthedocs.io.

Motivation
----------

My primary motivation for developing this package is because it contained a
piece of code I kept copy-pasting like a bloody caveman between projects.

My typical use-cases for this code include:

* Wrap the decoded JSON ``dict`` from crummy APIs without a schema that just
  decide to drop keys for which the values are ``null`` resulting in code with
  nested ``if "key" in result:``. This allowed me to either retrieve the value
  if it was there or at least arriving at an empty ``dict`` which evaluates to
  ``False`` when mapping their half-formed data to my own data-structures.
* Create arbitrarily-nested dictionaries of code stats that I can keep organized
  as I like while using in the code and then quickly ``flatten`` to a Graphite
  compatible format prior to posting them to ... well Graphite.

Basic Usage
-----------

This would be the typical Python ``dict`` behaviour when accessing a missing
key::

    >>> d = {"a": 1, "b": 2}
    >>> d["a"]
    1
    >>> d["missing"]
    ---------------------------------------------------------------------------
    KeyError                                  Traceback (most recent call last)
    <ipython-input-3-d4f58b57b715> in <module>()
    ----> 1 d["missing"]

    KeyError: 'missing'

While if we were using a ``VivoDict``, then upon accessing a missing key we
would be provided with an implicitly created empty ``VivoDict`` as such::

    >>> from vivodict import VivoDict
    >>> d = VivoDict.vivify({"a": 1, "b": 2})
    >>> d["a"]
    1
    >>> d["missing"]
    {}

.. note::

    Note that instantiation above is not performed simply by passing an existing
    ``dict`` to ``VivoDict`` but instead through the ``vivify`` class method
    which can recursively convert any arbitrarily-nested ``dict`` to a
    ``VivoDict``.

Now, while the above doesn't seem to offer anything a simple ``try-except`` or
a ``if "key" not in d`` wouldn't offer, the ``VivoDict`` becomes useful when
dealing with arbitrarily nested dictionaries where there may be several levels
of missing keys. For example::

    >>> from vivodict import VivoDict
    >>> d = VivoDict({"a": 1, "b": {"c": 2}, "d": {"e": {"f": 3}}})
    >>> d["a"]
    1
    >>> d["b"]["c"]
    2
    >>> d["d"]["e"]["f"]
    3
    >>> d["i"]["am"]["missing"]["eh"] = 4
    >>> d
    {'a': 1,
    'b': {'c': 2},
    'd': {'e': {'f': 3}},
    'i': {'am': {'missing': {'eh': 4}}}}

So, as can be seen, having auto-vivification allows one to nest keys and values
to whatever degree.

.. warning::

    The primary caveat of the above functionality is that ``VivoDict`` are very
    forgiving when it comes to typos which can leads to weird errors. A
    mistyped key will simply create a new ``VivoDict`` and will allow you to go
    down some rabbithole of erroneously typed keys your linter won't get you out
    of.

Convenience Functions
---------------------

In addition to the above, a few basic convenience methods have been built into
the ``VivoDict`` class, mostly cause they make my life easier and lazier.


flatten
^^^^^^^

As I mentioned prior one of my typical use-cases for ``vivodict`` is using it
to store nested metrics which I then post to Graphite via simple HTTP requests.

Graphite, however, bases its structure on ``.`` delimited names where anything
preceding a ``.`` is considered to be a metric folder with the last token being
the metric itself.

Thus, I needed a quick way to flatten a nested ``dict`` into a Graphite
compatible version.

The ``flatten`` method does exactly that::

    >>> d = VivoDict.vivify({"a": 1, "b": {"c": 2}, "d": {"e": {"f": 3}}})
    >>> d.flatten()
    {'a': 1, 'b.c': 2, 'd.e.f': 3}

replace
^^^^^^^

Following the same premise as with ``flatten`` I needed to quickly 'reset' my
metrics back to 0 between posting cycles.

Hence, ``replace`` will replace all 'leaf' node values in what is essentially a
tree with a given ``value``::

    >>> d = VivoDict.vivify({"a": 1, "b": {"c": 2}, "d": {"e": {"f": 3}}})
    >>> d.replace(replace_with=0)
    >>> d
    {'a': 0, 'b': {'c': 0}, 'd': {'e': {'f': 0}}}

.. warning::

    As you may have noticed from the above snippet, the ``replace`` method
    performs an **in-place** replacement instead of returning a copy of the
    original ``VivoDict`` with replaced values.

Should you need to maintain an original copy I'd suggest you use the ``copy``
package and its ``deepcopy`` function (cause Python passes by reference) as
such::

    >>> import copy
    >>> original = VivoDict.vivify({"a": 1, "b": {"c": 2}, "d": {"e": {"f": 3}}})
    >>> modified = copy.deepcopy(original)
    >>> modified.replace(replace_with=0)
    >>> original
    {'a': 1, 'b': {'c': 2}, 'd': {'e': {'f': 3}}}
    >>> modified
    {'a': 0, 'b': {'c': 0}, 'd': {'e': {'f': 0}}}


apply
^^^^^

Lastly, I often had to modify all values through a given function, typically
divide them by a number of observation for average metrics which can be easily
done through the ``apply`` method which can take any ``callable`` as an
argument and replace the original value with its return-value::

    >>> d = VivoDict.vivify({"a": 1, "b": {"c": 2}, "d": {"e": {"f": 3}}})
    >>> def double(value):
    >>>     return value * 2
    >>> d.apply(double)
    >>> d
    {'a': 2, 'b': {'c': 4}, 'd': {'e': {'f': 6}}}
    >>> d.apply(lambda value: value / 2)
    {'a': 1, 'b': {'c': 2}, 'd': {'e': {'f': 3}}}

.. warning::

    Much like ``replace``, the ``apply`` method replaces values **in-place**.


=======
History
=======

0.3.1 (2017-07-23)
------------------

* README.rst: Fixed minor formatting typo.

0.3.0 (2017-07-23)
------------------

* Cleanup the docos and removed a bunch of the unnecessary stuff.

0.2.0 (2017-07-23)
------------------

* Added more unit-tests and improved docstrings.

0.1.1 (2017-07-23)
------------------

* Fixed issues with the Python dependencies.

0.1.0 (2017-07-23)
------------------

* First release on PyPI.