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)
array([ 1, 2, 9223372036854775807,
9223372036854775807, 5])
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)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1402, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1401 with ak._errors.OperationErrorContext(name, inputs, kwargs):
-> 1402 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:451, in array_ufunc(ufunc, method, inputs, kwargs)
450 else:
--> 451 out = ak._broadcasting.broadcast_and_apply(
452 inputs, action, behavior, allow_records=False, function_name=ufunc.__name__
453 )
454 assert isinstance(out, tuple) and len(out) == 1
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:1022, 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)
1021 isscalar = []
-> 1022 out = apply_step(
1023 backend,
1024 broadcast_pack(inputs, isscalar),
1025 action,
1026 0,
1027 depth_context,
1028 lateral_context,
1029 behavior,
1030 {
1031 "allow_records": allow_records,
1032 "left_broadcast": left_broadcast,
1033 "right_broadcast": right_broadcast,
1034 "numpy_to_regular": numpy_to_regular,
1035 "regular_to_jagged": regular_to_jagged,
1036 "function_name": function_name,
1037 "broadcast_parameters_rule": broadcast_parameters_rule,
1038 },
1039 )
1040 assert isinstance(out, tuple)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:1001, in apply_step(backend, inputs, action, depth, depth_context, lateral_context, behavior, options)
1000 elif result is None:
-> 1001 return continuation()
1002 else:
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:974, in apply_step.<locals>.continuation()
973 elif any(x.is_list and not is_string_like(x) for x in contents):
--> 974 return broadcast_any_list()
976 # Any RecordArrays?
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_broadcasting.py:612, in apply_step.<locals>.broadcast_any_list()
611 else:
--> 612 raise ValueError(
613 "cannot broadcast RegularArray of size "
614 "{} with RegularArray of size {}{}".format(
615 x.size, dim_size, in_function(options)
616 )
617 )
618 else:
ValueError: cannot broadcast RegularArray of size 3 with RegularArray of size 5 in add
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
Cell In[25], line 1
----> 1 array + array[booleans]
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_operators.py:50, in _binary_method.<locals>.func(self, other)
48 if _disables_array_ufunc(other):
49 return NotImplemented
---> 50 return ufunc(self, other)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1401, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1336 """
1337 Intercepts attempts to pass this Array to a NumPy
1338 [universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)
(...)
1398 See also #__array_function__.
1399 """
1400 name = f"{type(ufunc).__module__}.{ufunc.__name__}.{method!s}"
-> 1401 with ak._errors.OperationErrorContext(name, inputs, kwargs):
1402 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:67, in ErrorContext.__exit__(self, exception_type, exception_value, traceback)
60 try:
61 # Handle caught exception
62 if (
63 exception_type is not None
64 and issubclass(exception_type, Exception)
65 and self.primary() is self
66 ):
---> 67 self.handle_exception(exception_type, exception_value)
68 finally:
69 # `_kwargs` may hold cyclic references, that we really want to avoid
70 # as this can lead to large buffers remaining in memory for longer than absolutely necessary
71 # Let's just clear this, now.
72 self._kwargs.clear()
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:82, in ErrorContext.handle_exception(self, cls, exception)
80 self.decorate_exception(cls, exception)
81 else:
---> 82 raise self.decorate_exception(cls, exception)
ValueError: cannot broadcast RegularArray of size 3 with RegularArray of size 5 in add
This error occurred while calling
numpy.add.__call__(
<Array [1, 2, 3, 4, 5] type='5 * int64'>
<Array [1, 2, 5] type='3 * int64'>
)
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
/tmp/ipykernel_6534/3685422949.py:2: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def example(builder):
[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
/tmp/ipykernel_6534/2461453671.py:2: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
def faster_example():
[1, 2, None, None, 5] ---------------- type: 5 * ?int64