ak.type#

ak.type(array, *, behavior=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
behavior (None or dict) – Custom ak.behavior for the output type, if high-level.

The high-level type of an array (many types supported, including all Awkward Arrays and Records) as ak.types.Type objects.

The high-level type ignores layout differences like ak.contents.ListArray versus ak.contents.ListOffsetArray, but not differences like “regular-sized lists” (i.e. ak.contents.RegularArray) versus “variable-sized lists” (i.e. ak.contents.ListArray and similar).

Types are rendered as Datashape strings, which makes the same distinctions.

For example,

>>> array = ak.Array([[{"x": 1.1, "y": [1]}, {"x": 2.2, "y": [2, 2]}],
...                   [],
...                   [{"x": 3.3, "y": [3, 3, 3]}]])

has type

>>> ak.type(array).show()
3 * var * {
    x: float64,
    y: var * int64
}

but

>>> array = ak.Array(np.arange(2*3*5).reshape(2, 3, 5))

has type

>>> ak.type(array).show()
2 * 3 * 5 * int64

Some cases, like heterogeneous data, require extensions beyond the Datashape specification. For example,

>>> array = ak.Array([1, "two", [3, 3, 3]])

has type

>>> ak.type(array).show()
3 * union[
    int64,
    string,
    var * int64
]

but “union” is not a Datashape type-constructor. (Its syntax is similar to existing type-constructors, so it’s a plausible addition to the language.)