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/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1356, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1355 with ak._errors.OperationErrorContext(name, inputs, kwargs):
-> 1356 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:317, in array_ufunc(ufunc, method, inputs, kwargs)
315 return result[0]
--> 317 out = ak._do.recursively_apply(
318 inputs[where],
319 unary_action,
320 behavior,
321 function_name=ufunc.__name__,
322 allow_records=False,
323 )
325 else:
File ~/micromamba/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/envs/awkward-docs/lib/python3.10/site-packages/awkward/contents/listarray.py:1557, in ListArray._recursively_apply(self, action, behavior, depth, depth_context, lateral_context, options)
1548 content._recursively_apply(
1549 action,
1550 behavior,
(...)
1554 options,
1555 )
-> 1557 result = action(
1558 self,
1559 depth=depth,
1560 depth_context=depth_context,
1561 lateral_context=lateral_context,
1562 continuation=continuation,
1563 behavior=behavior,
1564 backend=self._backend,
1565 options=options,
1566 )
1568 if isinstance(result, Content):
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:310, in array_ufunc.<locals>.unary_action(layout, **ignore)
309 nextinputs[where] = layout
--> 310 result = action(tuple(nextinputs), **ignore)
311 if result is None:
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_connect/numpy.py:290, in array_ufunc.<locals>.action(inputs, **ignore)
289 error_message.append(type(x).__name__)
--> 290 raise TypeError(
291 "no {}.{} overloads for custom types: {}".format(
292 type(ufunc).__module__, ufunc.__name__, ", ".join(error_message)
293 )
294 )
296 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/envs/awkward-docs/lib/python3.10/site-packages/awkward/highlevel.py:1355, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
1290 """
1291 Intercepts attempts to pass this Array to a NumPy
1292 [universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)
(...)
1352 See also #__array_function__.
1353 """
1354 name = f"{type(ufunc).__module__}.{ufunc.__name__}.{method!s}"
-> 1355 with ak._errors.OperationErrorContext(name, inputs, kwargs):
1356 return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:63, in ErrorContext.__exit__(self, exception_type, exception_value, traceback)
60 try:
61 # Handle caught exception
62 if exception_type is not None and self.primary() is self:
---> 63 self.handle_exception(exception_type, exception_value)
64 finally:
65 # `_kwargs` may hold cyclic references, that we really want to avoid
66 # as this can lead to large buffers remaining in memory for longer than absolutely necessary
67 # Let's just clear this, now.
68 self._kwargs.clear()
File ~/micromamba/envs/awkward-docs/lib/python3.10/site-packages/awkward/_errors.py:78, in ErrorContext.handle_exception(self, cls, exception)
76 self.decorate_exception(cls, exception)
77 else:
---> 78 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