Manipulate arrays of complex data structures as easily as Numpy.

awkward-array is a pure Python+Numpy library for manipulating complex data structures as you would Numpy arrays. Even if your data structures

• contain variable-length lists (jagged or ragged),
• are deeply nested (record structure),
• have different data types in the same list (heterogeneous),
• are masked, bit-masked, or index-mapped (nullable),
• contain cross-references or even cyclic references,
• need to be Python class instances on demand,
• are not defined at every point (sparse),
• are not contiguous in memory,
• should not be loaded into memory all at once (lazy),

this library can access them with the efficiency of Numpy arrays. They may be converted from JSON or Python data, loaded from "awkd" files, HDF5, Parquet, or ROOT files, or they may be views into memory buffers like Arrow.

Consider this monstrosity:

import awkward
array = awkward.fromiter([[1.1, 2.2, None, 3.3, None],
                          [4.4, [5.5]],
                          [{"x": 6, "y": {"z": 7}}, None, {"x": 8, "y": {"z": 9}}]
                         ])

It's a list of lists; the first contains numbers and None, the second contains a sub-sub-list, and the third defines nested records. If we print this out, we see that it is called a JaggedArray:

array
# returns <JaggedArray [[1.1 2.2 None 3.3 None] [4.4 [5.5]] [<Row 0> None <Row 1>]] at 79093e598f98>

and we get the full Python structure back by calling array.tolist():

array.tolist()
# returns [[1.1, 2.2, None, 3.3, None],
#          [4.4, [5.5]],
#          [{'x': 6, 'y': {'z': 7}}, None, {'x': 8, 'y': {'z': 9}}]]

But we can also manipulate it as though it were a Numpy array. We can, for instance, take the first two elements of each sub-list (slicing the second dimension):

array[:, :2]
# returns <JaggedArray [[1.1 2.2] [4.4 [5.5]] [<Row 0> None]] at 79093e5ab080>

or the last two:

array[:, -2:]
# returns <JaggedArray [[3.3 None] [4.4 [5.5]] [None <Row 1>]] at 79093e5ab3c8>

Internally, the data has been rearranged into a columnar form, with all values at a given level of hierarchy in the same array. Numpy-like slicing, masking, and fancy indexing are translated into Numpy operations on these internal arrays: they are not implemented with Python for loops!

To see some of this structure, ask for the content of the array:

array.content
# returns <IndexedMaskedArray [1.1 2.2 None ... <Row 0> None <Row 1>] at 79093e598ef0>

Notice that the boundaries between sub-lists are gone: they exist only at the JaggedArray level. This IndexedMaskedArray level handles the None values in the data. If we dig further, we'll find a UnionArray to handle the mixture of sub-lists and sub-sub-lists and record structures. If we dig deeply enough, we'll find the numerical data:

array.content.content.contents[0]
# returns array([1.1, 2.2, 3.3, 4.4])
array.content.content.contents[1].content
# returns array([5.5])

Perhaps most importantly, Numpy's universal functions (operations that apply to every element in an array) can be used on our array. This, too, goes straight to the columnar data and preserves structure.

array + 100
# returns <JaggedArray [[101.1 102.2 None 103.3 None]
#                       [104.4 [105.5]]
#                       [<Row 0> None <Row 1>]] at 724509ffe2e8>

(array + 100).tolist()
# returns [[101.1, 102.2, None, 103.3, None],
#          [104.4, [105.5]],
#          [{'x': 106, 'y': {'z': 107}}, None, {'x': 108, 'y': {'z': 109}}]]

numpy.sin(array)
# returns <JaggedArray [[0.8912073600614354 0.8084964038195901 None -0.1577456941432482 None]
#                       [-0.951602073889516 [-0.70554033]]
#                       [<Row 0> None <Row 1>]] at 70a40c3a61d0>

Rather than matching the speed of compiled code, this can exceed the speed of compiled code (on non-columnar data) because the operation may be vectorized on awkward-array's underlying columnar arrays.

(To do: performance example to substantiate that claim.)

Install awkward-array like any other Python package:

pip install awkward

or similar (use sudo, --user, virtualenv, or pip-in-conda if you wish).

• Python (2.7+, 3.4+)
• Numpy (1.13.1+)

• pyarrow to view Arrow and Parquet data as awkward-arrays
• h5py to read and write awkward-arrays in HDF5 files

(To do: integration with Dask, Pandas, and Numba.)

Table of contents:

• JSON log data processing example
• Features
  • Jaggedness
  • Record structure
  • Heterogeneous arrays
  • Masking
  • Cross-references
  • Class instances and methods
  • Indirection
  • Sparseness
  • Non-contiguousness
  • Laziness
• Serialization, reading and writing files
• Detailed particle physics examples
  • Jagged Lorentz vector arrays; Z peak
  • Particle isolation cuts
  • Generator/reconstructed matching

(Parquet exoplanets is in the serialization section.)

(...)