How to create arrays of missing data#
Data at any level of an Awkward Array can be “missing,” represented by None in Python.
This functionality is somewhat like NumPy’s masked arrays, but masked arrays can only declare numerical values to be missing (not, for instance, a row of a 2-dimensional array) and they represent missing data with an np.ma.masked object instead of None.
Pandas also handles missing data, but in several different ways. For floating point columns, NaN (not a number) is used to mean “missing,” and as of version 1.0, Pandas has a pd.NA object for missing data in other data types.
In Awkward Array, floating point NaN and a missing value are clearly distinct. Missing data, like all data in Awkward Arrays, are also not represented by any Python object; they are converted to and from None by ak.to_list() and ak.from_iter().
import awkward as ak
import numpy as np
From Python None#
The ak.Array constructor and ak.from_iter() interpret None as a missing value, and ak.to_list() converts them back into None.
ak.Array([1, 2, 3, None, 4, 5])
[1, 2, 3, None, 4, 5] ---------------- type: 6 * ?int64
The missing values can be deeply nested (missing integers):
ak.Array([[[[], [1, 2, None]]], [[[3]]], []])
[[[[], [1, 2, None]]], [[[3]]], []] ---------------------------------- type: 3 * var * var * var * ?int64
They can be shallow (missing lists):
ak.Array([[[[], [1, 2]]], None, [[[3]]], []])
[[[[], [1, 2]]], None, [[[3]]], []] ----------------------------------------- type: 4 * option[var * var * var * int64]
Or both:
ak.Array([[[[], [3]]], None, [[[None]]], []])
[[[[], [3]]], None, [[[None]]], []] ------------------------------------------ type: 4 * option[var * var * var * ?int64]
Records can also be missing:
ak.Array([{"x": 1, "y": 1}, None, {"x": 2, "y": 2}])
[{x: 1, y: 1},
None,
{x: 2, y: 2}]
--------------
type: 3 * ?{
x: int64,
y: int64
}Potentially missing values are represented in the type string as “?” or “option[...]” (if the nested type is a list, which needs to be bracketed for clarity).
From NumPy arrays#
Normal NumPy arrays can’t represent missing data, but masked arrays can. Here is how one is constructed in NumPy:
numpy_array = np.ma.MaskedArray([1, 2, 3, 4, 5], [False, False, True, True, False])
numpy_array
masked_array(data=[1, 2, --, --, 5],
mask=[False, False, True, True, False],
fill_value=999999)
It returns np.ma.masked objects if you try to access missing values:
numpy_array[0], numpy_array[1], numpy_array[2], numpy_array[3], numpy_array[4]
(1, 2, masked, masked, 5)
But it uses None for missing values in tolist:
numpy_array.tolist()
[1, 2, None, None, 5]
The ak.from_numpy() function converts masked arrays into Awkward Arrays with missing values, as does the ak.Array constructor.
awkward_array = ak.Array(numpy_array)
awkward_array
[1, 2, None, None, 5] ---------------- type: 5 * ?int64
The reverse, ak.to_numpy(), returns masked arrays if the Awkward Array has missing data.
ak.to_numpy(awkward_array)
masked_array(data=[1, 2, --, --, 5],
mask=[False, False, True, True, False],
fill_value=999999)
But np.asarray, the usual way of casting data as NumPy arrays, does not. (np.asarray is supposed to return a plain np.ndarray, which np.ma.masked_array is not.)
np.asarray(awkward_array)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[12], line 1
----> 1 np.asarray(awkward_array)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1319, in Array.__array__(self, *args, **kwargs)
1317 arguments.update(kwargs)
1318 with ak._errors.OperationErrorContext("numpy.asarray", arguments):
-> 1319 return ak._connect.numpy.convert_to_array(self._layout, args, kwargs)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:17, in convert_to_array(layout, args, kwargs)
16 def convert_to_array(layout, args, kwargs):
---> 17 out = ak.operations.to_numpy(layout, allow_missing=False)
18 if args == () and kwargs == {}:
19 return out
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/operations/ak_to_numpy.py:44, in to_numpy(array, allow_missing)
39 with ak._errors.OperationErrorContext(
40 "ak.to_numpy",
41 dict(array=array, allow_missing=allow_missing),
42 ):
43 with numpy.errstate(invalid="ignore"):
---> 44 return ak._util.to_arraylib(numpy, array, allow_missing)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_util.py:821, in to_arraylib(module, array, allow_missing)
818 layout = ak.operations.to_layout(array, allow_record=True, allow_other=True)
820 if isinstance(layout, (ak.contents.Content, ak.record.Record)):
--> 821 return layout.to_numpy(allow_missing=allow_missing)
822 else:
823 return module.asarray(array)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/contents/content.py:1015, in Content.to_numpy(self, allow_missing)
1014 def to_numpy(self, allow_missing: bool = True):
-> 1015 return self._to_numpy(allow_missing)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/contents/bytemaskedarray.py:985, in ByteMaskedArray._to_numpy(self, allow_missing)
984 def _to_numpy(self, allow_missing):
--> 985 return self.to_IndexedOptionArray64()._to_numpy(allow_missing)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/contents/indexedoptionarray.py:1502, in IndexedOptionArray._to_numpy(self, allow_missing)
1500 return numpy.ma.MaskedArray(data, mask)
1501 else:
-> 1502 raise ak._errors.wrap_error(
1503 ValueError(
1504 "ak.to_numpy cannot convert 'None' values to "
1505 "np.ma.MaskedArray unless the "
1506 "'allow_missing' parameter is set to True"
1507 )
1508 )
1509 else:
1510 if allow_missing:
ValueError: while calling
numpy.asarray(
<Array [1, 2, None, None, 5] type='5 * ?int64'>
)
Error details: ak.to_numpy cannot convert 'None' values to np.ma.MaskedArray unless the 'allow_missing' parameter is set to True
Missing rows vs missing numbers#
In Awkward Array, a missing list is a different thing from a list whose values are missing. However, ak.to_numpy() converts it for you.
missing_row = ak.Array([[1, 2, 3], None, [4, 5, 6]])
missing_row
[[1, 2, 3], None, [4, 5, 6]] ----------------------------- type: 3 * option[var * int64]
ak.to_numpy(missing_row)
masked_array(
data=[[1, 2, 3],
[--, --, --],
[4, 5, 6]],
mask=[[False, False, False],
[ True, True, True],
[False, False, False]],
fill_value=999999)
NaN is not missing#
Floating point NaN values are simply unrelated to missing values, in both Awkward Array and NumPy.
missing_with_nan = ak.Array([1.1, 2.2, np.nan, None, 3.3])
missing_with_nan
[1.1, 2.2, nan, None, 3.3] ------------------ type: 5 * ?float64
ak.to_numpy(missing_with_nan)
masked_array(data=[1.1, 2.2, nan, --, 3.3],
mask=[False, False, False, True, False],
fill_value=1e+20)
Missing values as empty lists#
Sometimes, it’s useful to think about a potentially missing value as a length-1 list if it is not missing and a length-0 list if it is. (Some languages define the option type as a kind of list.)
The Awkward functions ak.singletons() and ak.firsts() convert from “None form” to and from “lists form.”
none_form = ak.Array([1, 2, 3, None, None, 5])
none_form
[1, 2, 3, None, None, 5] ---------------- type: 6 * ?int64
lists_form = ak.singletons(none_form)
lists_form
[[1], [2], [3], [], [], [5]] --------------------- type: 6 * var * int64
ak.firsts(lists_form)
[1, 2, 3, None, None, 5] ---------------- type: 6 * ?int64
Masking instead of slicing#
The most common way of filtering data is to slice it with an array of booleans (usually the result of a calculation).
array = ak.Array([1, 2, 3, 4, 5])
array
[1, 2, 3, 4, 5] --------------- type: 5 * int64
booleans = ak.Array([True, True, False, False, True])
booleans
[True, True, False, False, True] -------------- type: 5 * bool
array[booleans]
[1, 2, 5] --------------- type: 3 * int64
The data can also be effectively filtered by replacing values with None. The following syntax does that:
array.mask[booleans]
[1, 2, None, None, 5] ---------------- type: 5 * ?int64
(Or use the ak.mask() function.)
An advantage of masking is that the length and nesting structure of the masked array is the same as the original array, so anything that broadcasts with one broadcasts with the other (so that unfiltered data can be used interchangeably with filtered data).
array + array.mask[booleans]
[2, 4, None, None, 10] ---------------- type: 5 * ?int64
whereas
array + array[booleans]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[25], line 1
----> 1 array + array[booleans]
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/numpy/lib/mixins.py:21, in _binary_method.<locals>.func(self, other)
19 if _disables_array_ufunc(other):
20 return NotImplemented
---> 21 return ufunc(self, other)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1392, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1390 arguments.update(kwargs)
1391 with ak._errors.OperationErrorContext(name, arguments):
-> 1392 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:261, in array_ufunc(ufunc, method, inputs, kwargs)
252 out = ak._do.recursively_apply(
253 inputs[where],
254 unary_action,
(...)
257 allow_records=False,
258 )
260 else:
--> 261 out = ak._broadcasting.broadcast_and_apply(
262 inputs, action, behavior, allow_records=False, function_name=ufunc.__name__
263 )
264 assert isinstance(out, tuple) and len(out) == 1
265 out = out[0]
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:1032, in broadcast_and_apply(inputs, action, behavior, depth_context, lateral_context, allow_records, left_broadcast, right_broadcast, numpy_to_regular, regular_to_jagged, function_name, broadcast_parameters_rule)
1030 backend = ak._backends.backend_of(*inputs)
1031 isscalar = []
-> 1032 out = apply_step(
1033 backend,
1034 broadcast_pack(inputs, isscalar),
1035 action,
1036 0,
1037 depth_context,
1038 lateral_context,
1039 behavior,
1040 {
1041 "allow_records": allow_records,
1042 "left_broadcast": left_broadcast,
1043 "right_broadcast": right_broadcast,
1044 "numpy_to_regular": numpy_to_regular,
1045 "regular_to_jagged": regular_to_jagged,
1046 "function_name": function_name,
1047 "broadcast_parameters_rule": broadcast_parameters_rule,
1048 },
1049 )
1050 assert isinstance(out, tuple)
1051 return tuple(broadcast_unpack(x, isscalar, backend) for x in out)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:1011, in apply_step(backend, inputs, action, depth, depth_context, lateral_context, behavior, options)
1009 return result
1010 elif result is None:
-> 1011 return continuation()
1012 else:
1013 raise ak._errors.wrap_error(AssertionError(result))
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:701, in apply_step.<locals>.continuation()
699 nextinputs.append(x.content[: x.length * x.size])
700 else:
--> 701 raise ak._errors.wrap_error(
702 ValueError(
703 "cannot broadcast RegularArray of size "
704 "{} with RegularArray of size {} {}".format(
705 x.size, maxsize, in_function(options)
706 )
707 )
708 )
709 else:
710 nextinputs.append(x)
ValueError: while calling
numpy.add.__call__(
<Array [1, 2, 3, 4, 5] type='5 * int64'>
<Array [1, 2, 5] type='3 * int64'>
)
Error details: cannot broadcast RegularArray of size 3 with RegularArray of size 5 in add
With ArrayBuilder#
ak.ArrayBuilder is described in more detail in this tutorial, but you can add missing values to an array using the null method or appending None.
(This is what ak.from_iter() uses internally to accumulate data.)
builder = ak.ArrayBuilder()
builder.append(1)
builder.append(2)
builder.null()
builder.append(None)
builder.append(3)
array = builder.snapshot()
array
[1, 2, None, None, 3] ---------------- type: 5 * ?int64
In Numba#
Functions that Numba Just-In-Time (JIT) compiles can use ak.ArrayBuilder or construct a boolean array for ak.mask().
(ak.ArrayBuilder can’t be constructed or converted to an array using snapshot inside a JIT-compiled function, but can be outside the compiled context.)
import numba as nb
@nb.jit
def example(builder):
builder.append(1)
builder.append(2)
builder.null()
builder.append(None)
builder.append(3)
return builder
builder = example(ak.ArrayBuilder())
array = builder.snapshot()
array
[1, 2, None, None, 3] ---------------- type: 5 * ?int64
@nb.jit
def faster_example():
data = np.empty(5, np.int64)
mask = np.empty(5, np.bool_)
data[0] = 1
mask[0] = True
data[1] = 2
mask[1] = True
mask[2] = False
mask[3] = False
data[4] = 5
mask[4] = True
return data, mask
data, mask = faster_example()
array = ak.Array(data).mask[mask]
array
[1, 2, None, None, 5] ---------------- type: 5 * ?int64