ak.to_categorical¶
Defined in awkward.behaviors.categorical on line 172.
- ak.to_categorical(array, highlevel=True)¶
- Parameters
array – Data convertible to an Awkward Array
highlevel (bool) – If True, return an
ak.Array
; otherwise, return a low-levelak.layout.Content
subclass.
Creates a categorical dataset, which has the following properties:
only distinct values (categories) are stored in their entirety,
pointers to those distinct values are represented by integers (an
ak.layout.IndexedArray
orak.layout.IndexedOptionArray
labeled with parameter"__array__" = "categorical"
.
This is equivalent to R’s “factor”, Pandas’s “categorical”, and
Arrow/Parquet’s “dictionary encoding.” It differs from generic uses of
ak.layout.IndexedArray
and ak.layout.IndexedOptionArray
in Awkward
Arrays by the guarantee of no duplicate categories and the "categorical"
parameter.
>>> array = ak.Array([["one", "two", "three"], [], ["three", "two"]])
>>> categorical = ak.to_categorical(array)
>>> categorical
<Array [['one', 'two', ... 'three', 'two']] type='3 * var * categorical[type=str...'>
>>> ak.type(categorical)
3 * var * categorical[type=string]
>>> ak.to_list(categorical) == ak.to_list(array)
True
>>> ak.categories(categorical)
<Array ['one', 'two', 'three'] type='3 * string'>
>>> ak.is_categorical(categorical)
True
>>> ak.from_categorical(categorical)
<Array [['one', 'two', ... 'three', 'two']] type='3 * var * string'>
This function descends through nested lists, but not into the fields of
records, so records can be categories. To make categorical record
fields, split up the record, apply this function to each desired field,
and ak.zip
the results together.
>>> records = ak.Array([
... {"x": 1.1, "y": "one"},
... {"x": 2.2, "y": "two"},
... {"x": 3.3, "y": "three"},
... {"x": 2.2, "y": "two"},
... {"x": 1.1, "y": "one"}
... ])
>>> records
<Array [{x: 1.1, y: 'one'}, ... y: 'one'}] type='5 * {"x": float64, "y": string}'>
>>> categorical_records = ak.zip({
... "x": ak.to_categorical(records["x"]),
... "y": ak.to_categorical(records["y"]),
... })
>>> categorical_records
<Array [{x: 1.1, y: 'one'}, ... y: 'one'}] type='5 * {"x": categorical[type=floa...'>
>>> ak.type(categorical_records)
5 * {"x": categorical[type=float64], "y": categorical[type=string]}
>>> ak.to_list(categorical_records) == ak.to_list(records)
True
The check for uniqueness is currently implemented in a Python loop, so conversion to categorical should be regarded as expensive. (This can change, but it would always be an _n log(n)_ operation.)
See also ak.is_categorical
, ak.categories
, ak.from_categorical
.