ak.ArrayBuilder#

Defined in awkward.highlevel on line 2573.

class ak.ArrayBuilder(self, *, behavior=None, attrs=None, initial=1024, resize=8)#
Parameters:

  • behavior (None or dict) – Custom ak.behavior for arrays built by this ArrayBuilder.

  • initial (int) – Initial size (in bytes) of buffers used by the ak::ArrayBuilder.

  • resize (float) – Resize multiplier for buffers used by the ak::ArrayBuilder; should be strictly greater than 1.

General tool for building arrays of nested data structures from a sequence of commands. Most data types can be constructed by calling commands in the right order, similar to printing tokens to construct JSON output.

To illustrate how this works, consider the following example.

b = ak.ArrayBuilder()

# fill commands   # as JSON   # current array type
##########################################################################################
b.begin_list()    # [         # 0 * var * unknown     (initially, the type is unknown)
b.integer(1)      #   1,      # 0 * var * int64
b.integer(2)      #   2,      # 0 * var * int64
b.real(3)         #   3.0     # 0 * var * float64     (all the integers have become floats)
b.end_list()      # ],        # 1 * var * float64     (closed first list; array length is 1)
b.begin_list()    # [         # 1 * var * float64
b.end_list()      # ],        # 2 * var * float64     (closed empty list; array length is 2)
b.begin_list()    # [         # 2 * var * float64
b.integer(4)      #   4,      # 2 * var * float64
b.null()          #   null,   # 2 * var * ?float64    (now the floats are nullable)
b.integer(5)      #   5       # 2 * var * ?float64
b.end_list()      # ],        # 3 * var * ?float64
b.begin_list()    # [         # 3 * var * ?float64
b.begin_record()  #   {       # 3 * var * union[?float64, ?{}]
b.field("x")      #     "x":  # 3 * var * union[?float64, ?{x: unknown}]
b.integer(1)      #      1,   # 3 * var * union[?float64, ?{x: int64}]
b.field("y")      #      "y": # 3 * var * union[?float64, ?{x: int64, y: unknown}]
b.begin_list()    #      [    # 3 * var * union[?float64, ?{x: int64, y: var * unknown}]
b.integer(2)      #        2, # 3 * var * union[?float64, ?{x: int64, y: var * int64}]
b.integer(3)      #        3  # 3 * var * union[?float64, ?{x: int64, y: var * int64}]
b.end_list()      #      ]    # 3 * var * union[?float64, ?{x: int64, y: var * int64}]
b.end_record()    #   }       # 3 * var * union[?float64, ?{x: int64, y: var * int64}]
b.end_list()      # ]         # 4 * var * union[?float64, ?{x: int64, y: var * int64}]

To get an array, we take a snapshot of the ArrayBuilder’s current state.

>>> b.snapshot()
<Array [[1, 2, 3], ..., [{x: 1, y: ..., ...}]] type='4 * var * union[?float...'>
>>> b.snapshot().show()
[[1, 2, 3],
 [],
 [4, None, 5],
 [{x: 1, y: [2, 3]}]]

The full set of filling commands is the following.

ArrayBuilders can be used in Numba: they can be passed as arguments to a Numba-compiled function or returned as return values. (Since ArrayBuilder works by accumulating side-effects, it’s not strictly necessary to return the object.)

The primary limitation is that ArrayBuilders cannot be created and snapshot cannot be called inside the Numba-compiled function. Awkward Array uses Numba as a transformer: ak.Array and an empty ak.ArrayBuilder go in and a filled ak.ArrayBuilder is the result; snapshot can be called outside of the compiled function.

Also, context managers (Python’s with statement) are not supported in Numba yet, so the list, tuple, and record methods are not available in Numba-compiled functions.

Here is an example of filling an ArrayBuilder in Numba, which makes a tree of dynamic depth.

>>> import numba as nb
>>> @nb.njit
... def deepnesting(builder, probability):
...     if np.random.uniform(0, 1) > probability:
...         builder.append(np.random.normal())
...     else:
...         builder.begin_list()
...         for i in range(np.random.poisson(3)):
...             deepnesting(builder, probability**2)
...         builder.end_list()
...
>>> builder = ak.ArrayBuilder()
>>> deepnesting(builder, 0.9)
>>> builder.snapshot()
<Array [[[-0.523, ..., [[2.16, ...], ...]]]] type='1 * var * var * union[fl...'>
>>> builder.type.show()
1 * var * var * union[
    float64,
    var * union[
        var * union[
            float64,
            var * unknown
        ],
        float64
    ]
]

Note that this is a general method for building arrays; if the type is known in advance, more specialized procedures can be faster. This should be considered the “least effort” approach.

ak.ArrayBuilder._wrap(cls, layout, behavior=None, attrs=None)#
Parameters:

  • layout (ak._ext.ArrayBuilder) – Low-level builder to wrap.

  • behavior (None or dict) – Custom ak.behavior for arrays built by this ArrayBuilder.

Wraps a low-level ak._ext.ArrayBuilder as a high-level ak.ArrayBulider.

The ak.ArrayBuilder constructor creates a new ak._ext.ArrayBuilder with no accumulated data, but Numba needs to wrap existing data when returning from a lowered function.

ak.ArrayBuilder.attrs#

The mapping containing top-level metadata, which is serialised with the array during pickling.

Keys prefixed with @ are identified as “transient” attributes which are discarded prior to pickling, permitting the storage of non-pickleable types.

ak.ArrayBuilder.behavior#

The behavior parameter passed into this ArrayBuilder’s constructor.

  • If a dict, this behavior overrides the global ak.behavior.

    Any keys in the global ak.behavior but not this behavior are still valid, but any keys in both are overridden by this behavior. Keys with a None value are equivalent to missing keys, so this behavior can effectively remove keys from the global ak.behavior.

  • If None, the Array defaults to the global ak.behavior.

See ak.behavior for a list of recognized key patterns and their meanings.

ak.ArrayBuilder.tolist(self)#

Converts this Array into Python objects; same as ak.to_list (but without the underscore, like NumPy’s tolist).

ak.ArrayBuilder.to_list(self)#

Converts this Array into Python objects; same as ak.to_list.

ak.ArrayBuilder.to_numpy(self, allow_missing=True)#

Converts this Array into a NumPy array, if possible; same as ak.to_numpy.

ak.ArrayBuilder.type#

The high-level type of the accumulated array; same as ak.type.

Note that the outermost element of an Array’s type is always an ak.types.ArrayType, which specifies the number of elements in the array.

The type of a ak.contents.Content (from ak.Array.layout) is not wrapped by an ak.types.ArrayType.

ak.ArrayBuilder.typestr#

The high-level type of this accumulated array, presented as a string.

ak.ArrayBuilder.__len__(self)#

The current length of the accumulated array.

ak.ArrayBuilder.__str__(self)#
ak.ArrayBuilder.__repr__(self)#
ak.ArrayBuilder._repr(self, limit_cols)#
ak.ArrayBuilder.show(self, limit_rows=20, limit_cols=80, type=False, stream=STDOUT, *, formatter=None, precision=3)#
Parameters:

  • limit_rows (int) – Maximum number of rows (lines) to use in the output.

  • limit_cols (int) – Maximum number of columns (characters wide).

  • type (bool) – If True, print the type as well. (Doesn’t count toward number of rows/lines limit.)

  • stream (object with a ``write(str)`` method or None) – Stream to write the output to. If None, return a string instead of writing to a stream.

  • formatter (Mapping or None) – Mapping of types/type-classes to string formatters. If None, use the default formatter.

Display the contents of the array within limit_rows and limit_cols, using ellipsis (...) for hidden nested data.

The formatter argument controls the formatting of individual values, c.f. https://numpy.org/doc/stable/reference/generated/numpy.set_printoptions.html As Awkward Array does not implement strings as a NumPy dtype, the numpystr key is ignored; instead, a "bytes" and/or "str" key is considered when formatting string values, falling back upon "str_kind".

This method takes a snapshot of the data and calls show on it, and a snapshot copies data.

ak.ArrayBuilder.__array__(self, dtype=None, copy=None)#

Intercepts attempts to convert a snapshot of this array into a NumPy array and either performs a conversion if possible or raises an error.

See ak.Array.__array__ for a more complete description.

ak.ArrayBuilder.__arrow_array__(self, type=None)#
ak.ArrayBuilder.numba_type#

The type of this Array when it is used in Numba. It contains enough information to generate low-level code for accessing any element, down to the leaves.

See Numba documentation on types and signatures.

ak.ArrayBuilder.__bool__(self)#
ak.ArrayBuilder.snapshot(self)#

Converts the currently accumulated data into an ak.Array.

The currently accumulated data are copied into the new array.

ak.ArrayBuilder.null(self)#

Appends a None value at the current position in the accumulated array.

ak.ArrayBuilder.boolean(self, x)#

Appends a boolean value x at the current position in the accumulated array.

ak.ArrayBuilder.integer(self, x)#

Appends an integer x at the current position in the accumulated array.

ak.ArrayBuilder.real(self, x)#

Appends a floating point number x at the current position in the accumulated array.

ak.ArrayBuilder.complex(self, x)#

Appends a floating point number x at the current position in the accumulated array.

ak.ArrayBuilder.datetime(self, x)#

Appends a datetime value x at the current position in the accumulated array.

ak.ArrayBuilder.timedelta(self, x)#

Appends a timedelta value x at the current position in the accumulated array.

ak.ArrayBuilder.bytestring(self, x)#

Appends an unencoded string (raw bytes) x at the current position in the accumulated array.

ak.ArrayBuilder.string(self, x)#

Appends a UTF-8 encoded string x at the current position in the accumulated array.

ak.ArrayBuilder.begin_list(self)#

Begins filling a list; must be closed with end_list.

For example,

>>> builder = ak.ArrayBuilder()
>>> builder.begin_list()
>>> builder.real(1.1)
>>> builder.real(2.2)
>>> builder.real(3.3)
>>> builder.end_list()
>>> builder.begin_list()
>>> builder.end_list()
>>> builder.begin_list()
>>> builder.real(4.4)
>>> builder.real(5.5)
>>> builder.end_list()

produces

>>> builder.show()
[[1.1, 2.2, 3.3],
 [],
 [4.4, 5.5]]
ak.ArrayBuilder.end_list(self)#

Ends a list.

ak.ArrayBuilder.begin_tuple(self, numfields)#

Begins filling a tuple with numfields fields; must be closed with end_tuple.

For example,

>>> builder = ak.ArrayBuilder()
>>> builder.begin_tuple(3)
>>> builder.index(0).integer(1)
>>> builder.index(1).real(1.1)
>>> builder.index(2).string("one")
>>> builder.end_tuple()
>>> builder.begin_tuple(3)
>>> builder.index(0).integer(2)
>>> builder.index(1).real(2.2)
>>> builder.index(2).string("two")
>>> builder.end_tuple()

produces

>>> builder.show()
[(1, 1.1, 'one'),
 (2, 2.2, 'two')]
ak.ArrayBuilder.index(self, i)#
Parameters:

i (int) – The tuple slot to fill.

This method also returns the ak.ArrayBuilder, so that it can be chained with the value that fills the slot.

Prepares to fill a tuple slot; see begin_tuple for an example.

ak.ArrayBuilder.end_tuple(self)#

Ends a tuple.

ak.ArrayBuilder.begin_record(self, name=None)#

Begins filling a record with an optional name; must be closed with end_record.

For example,

>>> builder = ak.ArrayBuilder()
>>> builder.begin_record("points")
>>> builder.field("x").real(1)
>>> builder.field("y").real(1.1)
>>> builder.end_record()
>>> builder.begin_record("points")
>>> builder.field("x").real(2)
>>> builder.field("y").real(2.2)
>>> builder.end_record()

produces

>>> builder.show()
[{x: 1, y: 1.1},
 {x: 2, y: 2.2}]

with type

>>> builder.type.show()
2 * points[
    x: float64,
    y: float64
]

The record type is named "points" because its "__record__" parameter is set to that value:

>>> builder.snapshot().layout.parameters
{'__record__': 'points'}

The "__record__" parameter can be used to add behavior to the records in the array, as described in ak.Array, ak.Record, and ak.behavior.

ak.ArrayBuilder.field(self, key)#
Parameters:

key (str) – The field key to fill.

This method also returns the ak.ArrayBuilder, so that it can be chained with the value that fills the slot.

Prepares to fill a field; see begin_record for an example.

ak.ArrayBuilder.end_record(self)#

Ends a record.

ak.ArrayBuilder.append(self, obj)#
Parameters:

obj – The data to append (None, bool, int, float, bytes, str, or anything recognized by ak.from_iter).

Appends any type, which can be a shorthand for null, boolean, integer, real, bytestring, or string, but also an ak.Array or ak.Record to reference values from an existing dataset, or any Python object to convert to Awkward Array.

If obj is an iterable (including dict), this is equivalent to ak.from_iter except that it fills an existing ak.ArrayBuilder, rather than creating a new one.

ak.ArrayBuilder.extend(self, obj)#
Parameters:

obj (iterable) – Iterable of data to extend this ArrayBuilder with.

Appends every value from obj.

ak.ArrayBuilder.list(self)#

Context manager to prevent unpaired begin_list and end_list. The example in the begin_list documentation can be rewritten as

>>> builder = ak.ArrayBuilder()
>>> with builder.list():
...     builder.real(1.1)
...     builder.real(2.2)
...     builder.real(3.3)
...
>>> with builder.list():
...     pass
...
>>> with builder.list():
...     builder.real(4.4)
...     builder.real(5.5)
...

to produce the same result.

>>> builder.show()
[[1.1, 2.2, 3.3],
 [],
 [4.4, 5.5]]

Since context managers aren’t yet supported by Numba, this method can’t be used in Numba.

ak.ArrayBuilder.tuple(self, numfields)#

Context manager to prevent unpaired begin_tuple and end_tuple. The example in the begin_tuple documentation can be rewritten as

>>> builder = ak.ArrayBuilder()
>>> with builder.tuple(3):
...     builder.index(0).integer(1)
...     builder.index(1).real(1.1)
...     builder.index(2).string("one")
...
>>> with builder.tuple(3):
...     builder.index(0).integer(2)
...     builder.index(1).real(2.2)
...     builder.index(2).string("two")
...

to produce the same result.

>>> builder.show()
[(1, 1.1, 'one'),
 (2, 2.2, 'two')]

Since context managers aren’t yet supported by Numba, this method can’t be used in Numba.

ak.ArrayBuilder.record(self, name=None)#

Context manager to prevent unpaired begin_record and end_record. The example in the begin_record documentation can be rewritten as

>>> builder = ak.ArrayBuilder()
>>> with builder.record("points"):
...     builder.field("x").real(1)
...     builder.field("y").real(1.1)
...
>>> with builder.record("points"):
...     builder.field("x").real(2)
...     builder.field("y").real(2.2)
...

to produce the same result.

>>> builder.show()
[{x: 1, y: 1.1},
 {x: 2, y: 2.2}]

Since context managers aren’t yet supported by Numba, this method can’t be used in Numba.