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:1360, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1359 with ak._errors.OperationErrorContext(name, arguments):
-> 1360 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:318, in array_ufunc(ufunc, method, inputs, kwargs)
316 return result[0]
--> 318 out = ak._do.recursively_apply(
319 inputs[where],
320 unary_action,
321 behavior,
322 function_name=ufunc.__name__,
323 allow_records=False,
324 )
326 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:1495, in ListArray._recursively_apply(self, action, behavior, depth, depth_context, lateral_context, options)
1486 content._recursively_apply(
1487 action,
1488 behavior,
(...)
1492 options,
1493 )
-> 1495 result = action(
1496 self,
1497 depth=depth,
1498 depth_context=depth_context,
1499 lateral_context=lateral_context,
1500 continuation=continuation,
1501 behavior=behavior,
1502 backend=self._backend,
1503 options=options,
1504 )
1506 if isinstance(result, Content):
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:311, in array_ufunc.<locals>.unary_action(layout, **ignore)
310 nextinputs[where] = layout
--> 311 result = action(tuple(nextinputs), **ignore)
312 if result is None:
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:291, in array_ufunc.<locals>.action(inputs, **ignore)
290 error_message.append(type(x).__name__)
--> 291 raise TypeError(
292 "no {}.{} overloads for custom types: {}".format(
293 type(ufunc).__module__, ufunc.__name__, ", ".join(error_message)
294 )
295 )
297 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:1359, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1357 arguments[i] = arg
1358 arguments.update(kwargs)
-> 1359 with ak._errors.OperationErrorContext(name, arguments):
1360 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 # Step out of the way so that another ErrorContext can become primary.
59 if self.primary() is self:
File ~/micromamba-root/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:66, in ErrorContext.handle_exception(self, cls, exception)
64 self.decorate_exception(cls, exception)
65 else:
---> 66 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