NEP 34 — Disallow inferring dtype=object from sequences#


Matti Picus




Standards Track





When users create arrays with sequences-of-sequences, they sometimes err in matching the lengths of the nested sequences, commonly called “ragged arrays”. Here we will refer to them as ragged nested sequences. Creating such arrays via np.array([<ragged_nested_sequence>]) with no dtype keyword argument will today default to an object-dtype array. Change the behaviour to raise a ValueError instead.

Motivation and scope#

Users who specify lists-of-lists when creating a numpy.ndarray via np.array may mistakenly pass in lists of different lengths. Currently we accept this input and automatically create an array with dtype=object. This can be confusing, since it is rarely what is desired. Changing the automatic dtype detection to never return object for ragged nested sequences (defined as a recursive sequence of sequences, where not all the sequences on the same level have the same length) will force users who actually wish to create object arrays to specify that explicitly. Note that lists, tuples, and nd.ndarrays are all sequences [0]. See for instance issue 5303.

Usage and impact#

After this change, array creation with ragged nested sequences must explicitly define a dtype:

>>> np.array([[1, 2], [1]])
ValueError: cannot guess the desired dtype from the input
>>> np.array([[1, 2], [1]], dtype=object)
# succeeds, with no change from current behaviour

The deprecation will affect any call that internally calls np.asarray. For instance, the assert_equal family of functions calls np.asarray, so users will have to change code like:

np.assert_equal(a, [[1, 2], 3])


np.assert_equal(a, np.array([[1, 2], 3], dtype=object))

Detailed description#

To explicitly set the shape of the object array, since it is sometimes hard to determine what shape is desired, one could use:

>>> arr = np.empty(correct_shape, dtype=object)
>>> arr[...] = values

We will also reject mixed sequences of non-sequence and sequence, for instance all of these will be rejected:

>>> arr = np.array([np.arange(10), [10]])
>>> arr = np.array([[range(3), range(3), range(3)], [range(3), 0, 0]])


The code to be changed is inside PyArray_GetArrayParamsFromObject and the internal discover_dimensions function. The first implementation in PR 14794 caused a number of downstream library failures and was reverted before the release of 1.18. Subsequently downstream libraries fixed the places they were using ragged arrays. The reimplementation became PR 15119 which was merged for the 1.19 release.

Backward compatibility#

Anyone depending on creating object arrays from ragged nested sequences will need to modify their code. There will be a deprecation period during which the current behaviour will emit a DeprecationWarning.


  • We could continue with the current situation.

  • It was also suggested to add a kwarg depth to array creation, or perhaps to add another array creation API function ragged_array_object. The goal was to eliminate the ambiguity in creating an object array from array([[1, 2], [1]], dtype=object): should the returned array have a shape of (1,), or (2,)? This NEP does not deal with that issue, and only deprecates the use of array with no dtype=object for ragged nested sequences. Users of ragged nested sequences may face another deprecation cycle in the future. Rationale: we expect that there are very few users who intend to use ragged arrays like that, this was never intended as a use case of NumPy arrays. Users are likely better off with another library or just using list of lists.

  • It was also suggested to deprecate all automatic creation of object-dtype arrays, which would require adding an explicit dtype=object for something like np.array([Decimal(10), Decimal(10)]). This too is out of scope for the current NEP. Rationale: it’s harder to asses the impact of this larger change, we’re not sure how many users this may impact.


Comments to issue 5303 indicate this is unintended behaviour as far back as 2014. Suggestions to change it have been made in the ensuing years, but none have stuck. The WIP implementation in PR 14794 seems to point to the viability of this approach.

References and footnotes#