How to create arrays of strings#
Awkward Arrays can contain strings, although these strings are just a special view of lists of uint8 numbers. As such, the variable-length data are efficiently stored.
NumPy’s strings are padded to have equal width, and Pandas’s strings are Python objects. Awkward Array doesn’t have nearly as many functions for manipulating arrays of strings as NumPy and Pandas, though.
import awkward as ak
import numpy as np
From Python strings#
The ak.Array constructor and ak.from_iter() recognize strings, and strings are returned by ak.to_list().
ak.Array(["one", "two", "three"])
['one', 'two', 'three'] ---------------- type: 3 * string
They may be nested within anything.
ak.Array([["one", "two"], [], ["three"]])
[['one', 'two'], [], ['three']] ---------------------- type: 3 * var * string
From NumPy arrays#
NumPy strings are also recognized by ak.from_numpy() and ak.to_numpy().
numpy_array = np.array(["one", "two", "three", "four"])
numpy_array
array(['one', 'two', 'three', 'four'], dtype='<U5')
awkward_array = ak.Array(numpy_array)
awkward_array
['one', 'two', 'three', 'four'] ---------------- type: 4 * string
Operations with strings#
Since strings are really just lists, some of the list operations “just work” on strings.
ak.num(awkward_array)
[3, 3, 5, 4] --------------- type: 4 * int64
awkward_array[:, 1:]
['ne', 'wo', 'hree', 'our'] ---------------- type: 4 * string
Others had to be specially overloaded for the string case, such as string-equality. The default meaning for == would be to descend to the lowest level and compare numbers (characters, in this case).
awkward_array == "three"
[False, False, True, False] -------------- type: 4 * bool
awkward_array == ak.Array(["ONE", "TWO", "three", "four"])
[False, False, True, True] -------------- type: 4 * bool
Similarly, ak.sort() and ak.argsort() sort strings lexicographically, not individual characters.
ak.sort(awkward_array)
['four', 'one', 'three', 'two'] ---------------- type: 4 * string
Still other operations had to be inhibited, since they wouldn’t make sense for strings.
np.sqrt(awkward_array)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1362, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1361 with ak._errors.OperationErrorContext(name, arguments):
-> 1362 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:312, in array_ufunc(ufunc, method, inputs, kwargs)
310 return result[0]
--> 312 out = ak._do.recursively_apply(
313 inputs[where],
314 unary_action,
315 behavior,
316 function_name=ufunc.__name__,
317 allow_records=False,
318 )
320 else:
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_do.py:35, in recursively_apply(layout, action, behavior, depth_context, lateral_context, allow_records, keep_parameters, numpy_to_regular, return_simplified, return_array, function_name, regular_to_jagged)
34 if isinstance(layout, Content):
---> 35 return layout._recursively_apply(
36 action,
37 behavior,
38 1,
39 copy.copy(depth_context),
40 lateral_context,
41 {
42 "allow_records": allow_records,
43 "keep_parameters": keep_parameters,
44 "numpy_to_regular": numpy_to_regular,
45 "regular_to_jagged": regular_to_jagged,
46 "return_simplified": return_simplified,
47 "return_array": return_array,
48 "function_name": function_name,
49 },
50 )
52 elif isinstance(layout, Record):
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/contents/listarray.py:1513, in ListArray._recursively_apply(self, action, behavior, depth, depth_context, lateral_context, options)
1504 content._recursively_apply(
1505 action,
1506 behavior,
(...)
1510 options,
1511 )
-> 1513 result = action(
1514 self,
1515 depth=depth,
1516 depth_context=depth_context,
1517 lateral_context=lateral_context,
1518 continuation=continuation,
1519 behavior=behavior,
1520 backend=self._backend,
1521 options=options,
1522 )
1524 if isinstance(result, Content):
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:305, in array_ufunc.<locals>.unary_action(layout, **ignore)
304 nextinputs[where] = layout
--> 305 result = action(tuple(nextinputs), **ignore)
306 if result is None:
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:285, in array_ufunc.<locals>.action(inputs, **ignore)
284 error_message.append(type(x).__name__)
--> 285 raise TypeError(
286 "no {}.{} overloads for custom types: {}".format(
287 type(ufunc).__module__, ufunc.__name__, ", ".join(error_message)
288 )
289 )
291 return None
TypeError: no numpy.sqrt overloads for custom types: string
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
Cell In[11], line 1
----> 1 np.sqrt(awkward_array)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1361, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1359 arguments[i] = arg
1360 arguments.update(kwargs)
-> 1361 with ak._errors.OperationErrorContext(name, arguments):
1362 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:56, in ErrorContext.__exit__(self, exception_type, exception_value, traceback)
53 try:
54 # Handle caught exception
55 if exception_type is not None and self.primary() is self:
---> 56 self.handle_exception(exception_type, exception_value)
57 finally:
58 # `_kwargs` may hold cyclic references, that we really want to avoid
59 # as this can lead to large buffers remaining in memory for longer than absolutely necessary
60 # Let's just clear this, now.
61 self._kwargs.clear()
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:71, in ErrorContext.handle_exception(self, cls, exception)
69 self.decorate_exception(cls, exception)
70 else:
---> 71 raise self.decorate_exception(cls, exception)
TypeError: no numpy.sqrt overloads for custom types: string
This error occurred while calling
numpy.sqrt.__call__(
<Array ['one', 'two', 'three', 'four'] type='4 * string'>
)
Categorical strings#
A large set of strings with few unique values are more efficiently manipulated as integers than as strings. In Pandas, this is categorical data, in R, it’s called a factor, and in Arrow and Parquet, it’s dictionary encoding.
The ak.to_categorical() function makes Awkward Arrays categorical in this sense. ak.to_arrow() and ak.to_parquet() recognize categorical data and convert it to the corresponding Arrow and Parquet types.
uncategorized = ak.Array(["three", "one", "two", "two", "three", "one", "one", "one"])
uncategorized
['three', 'one', 'two', 'two', 'three', 'one', 'one', 'one'] ---------------- type: 8 * string
categorized = ak.to_categorical(uncategorized)
categorized
['three', 'one', 'two', 'two', 'three', 'one', 'one', 'one'] ---------------------------------- type: 8 * categorical[type=string]
Internally, the data now have an index that selects from a set of unique strings.
categorized.layout.index
<Index dtype='int64' len='8'>[0 1 2 2 0 1 1 1]</Index>
ak.Array(categorized.layout.content)
['three', 'one', 'two'] ---------------- type: 3 * string
The main advantage to Awkward categorical data (other than proper conversions to Arrow and Parquet) is that equality is performed using the index integers.
categorized == "one"
[False, True, False, False, False, True, True, True] -------------- type: 8 * bool
With ArrayBuilder#
ak.ArrayBuilder() is described in more detail in this tutorial, but you can add strings by calling the string method or simply appending them.
(This is what ak.from_iter() uses internally to accumulate data.)
builder = ak.ArrayBuilder()
builder.string("one")
builder.append("two")
builder.append("three")
array = builder.snapshot()
array
['one', 'two', 'three'] ---------------- type: 3 * string