compute.py

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.

from pyarrow._compute import ( # noqa
 Function,
 FunctionOptions,
 FunctionRegistry,
 HashAggregateFunction,
 HashAggregateKernel,
 Kernel,
 ScalarAggregateFunction,
 ScalarAggregateKernel,
 ScalarFunction,
 ScalarKernel,
 VectorFunction,
 VectorKernel,
 # Option classes
 ArraySortOptions,
 AssumeTimezoneOptions,
 CastOptions,
 CountOptions,
 CumulativeOptions,
 CumulativeSumOptions,
 DayOfWeekOptions,
 DictionaryEncodeOptions,
 RunEndEncodeOptions,
 ElementWiseAggregateOptions,
 ExtractRegexOptions,
 ExtractRegexSpanOptions,
 FilterOptions,
 IndexOptions,
 InversePermutationOptions,
 JoinOptions,
 ListSliceOptions,
 ListFlattenOptions,
 MakeStructOptions,
 MapLookupOptions,
 MatchSubstringOptions,
 ModeOptions,
 NullOptions,
 PadOptions,
 PairwiseOptions,
 PartitionNthOptions,
 PivotWiderOptions,
 QuantileOptions,
 RandomOptions,
 RankOptions,
 RankQuantileOptions,
 ReplaceSliceOptions,
 ReplaceSubstringOptions,
 RoundBinaryOptions,
 RoundOptions,
 RoundTemporalOptions,
 RoundToMultipleOptions,
 ScalarAggregateOptions,
 ScatterOptions,
 SelectKOptions,
 SetLookupOptions,
 SkewOptions,
 SliceOptions,
 SortOptions,
 SplitOptions,
 SplitPatternOptions,
 StrftimeOptions,
 StrptimeOptions,
 StructFieldOptions,
 TakeOptions,
 TDigestOptions,
 TrimOptions,
 Utf8NormalizeOptions,
 VarianceOptions,
 WeekOptions,
 WinsorizeOptions,
 ZeroFillOptions,
 # Functions
 call_function,
 function_registry,
 get_function,
 list_functions,
 # Udf
 call_tabular_function,
 register_scalar_function,
 register_tabular_function,
 register_aggregate_function,
 register_vector_function,
 UdfContext,
 # Expressions
 Expression,
)

from collections import namedtuple
import inspect
from textwrap import dedent
import warnings

import pyarrow as pa
from pyarrow import _compute_docstrings
from pyarrow.vendored import docscrape


def _get_arg_names(func):
 return func._doc.arg_names


_OptionsClassDoc = namedtuple('_OptionsClassDoc', ('params',))


def _scrape_options_class_doc(options_class):
 if not options_class.__doc__:
 return None
 doc = docscrape.NumpyDocString(options_class.__doc__)
 return _OptionsClassDoc(doc['Parameters'])


def _decorate_compute_function(wrapper, exposed_name, func, options_class):
 # Decorate the given compute function wrapper with useful metadata
 # and documentation.
 cpp_doc = func._doc

 wrapper.__arrow_compute_function__ = dict(
 name=func.name,
 arity=func.arity,
 options_class=cpp_doc.options_class,
 options_required=cpp_doc.options_required)
 wrapper.__name__ = exposed_name
 wrapper.__qualname__ = exposed_name

 doc_pieces = []

 # 1. One-line summary
 summary = cpp_doc.summary
 if not summary:
 arg_str = "arguments" if func.arity > 1 else "argument"
 summary = f"Call compute function {func.name!r} with the given {arg_str}"

 doc_pieces.append(f"{summary}.\n\n")

 # 2. Multi-line description
 description = cpp_doc.description
 if description:
 doc_pieces.append(f"{description}\n\n")

 doc_addition = _compute_docstrings.function_doc_additions.get(func.name)

 # 3. Parameter description
 doc_pieces.append(dedent("""\
 Parameters
 ----------
 """))

 # 3a. Compute function parameters
 arg_names = _get_arg_names(func)
 for arg_name in arg_names:
 if func.kind in ('vector', 'scalar_aggregate'):
 arg_type = 'Array-like'
 else:
 arg_type = 'Array-like or scalar-like'
 doc_pieces.append(f"{arg_name} : {arg_type}\n")
 doc_pieces.append(" Argument to compute function.\n")

 # 3b. Compute function option values
 if options_class is not None:
 options_class_doc = _scrape_options_class_doc(options_class)
 if options_class_doc:
 for p in options_class_doc.params:
 doc_pieces.append(f"{p.name} : {p.type}\n")
 for s in p.desc:
 doc_pieces.append(f" {s}\n")
 else:
 warnings.warn(f"Options class {options_class.__name__} "
 f"does not have a docstring", RuntimeWarning)
 options_sig = inspect.signature(options_class)
 for p in options_sig.parameters.values():
 doc_pieces.append(dedent(f"""\
 {p.name} : optional
 Parameter for {options_class.__name__} constructor. Either `options`
 or `{p.name}` can be passed, but not both at the same time.
 """))
 doc_pieces.append(dedent(f"""\
 options : pyarrow.compute.{options_class.__name__}, optional
 Alternative way of passing options.
 """))

 doc_pieces.append(dedent("""\
 memory_pool : pyarrow.MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.
 """))

 # 4. Custom addition (e.g. examples)
 if doc_addition is not None:
 stripped = dedent(doc_addition).strip('\n')
 doc_pieces.append(f"\n{stripped}\n")

 wrapper.__doc__ = "".join(doc_pieces)
 return wrapper


def _get_options_class(func):
 class_name = func._doc.options_class
 if not class_name:
 return None
 try:
 return globals()[class_name]
 except KeyError:
 warnings.warn(f"Python binding for {class_name} not exposed",
 RuntimeWarning)
 return None


def _handle_options(name, options_class, options, args, kwargs):
 if args or kwargs:
 if options is not None:
 raise TypeError(
 f"Function {name!r} called with both an 'options' argument "
 f"and additional arguments")
 return options_class(*args, **kwargs)

 if options is not None:
 if isinstance(options, dict):
 return options_class(**options)
 elif isinstance(options, options_class):
 return options
 raise TypeError(
 f"Function {name!r} expected a {options_class} parameter, "
 f"got {type(options)}")

 return None


def _make_generic_wrapper(func_name, func, options_class, arity):
 if options_class is None:
 def wrapper(*args, memory_pool=None):
 if arity is not Ellipsis and len(args) != arity:
 raise TypeError(
 f"{func_name} takes {arity} positional argument(s), "
 f"but {len(args)} were given"
 )
 if args and isinstance(args[0], Expression):
 return Expression._call(func_name, list(args))
 return func.call(args, None, memory_pool)
 else:
 def wrapper(*args, memory_pool=None, options=None, **kwargs):
 if arity is not Ellipsis:
 if len(args) < arity:
 raise TypeError(
 f"{func_name} takes {arity} positional argument(s), "
 f"but {len(args)} were given"
 )
 option_args = args[arity:]
 args = args[:arity]
 else:
 option_args = ()
 options = _handle_options(func_name, options_class, options,
 option_args, kwargs)
 if args and isinstance(args[0], Expression):
 return Expression._call(func_name, list(args), options)
 return func.call(args, options, memory_pool)
 return wrapper


def _make_signature(arg_names, var_arg_names, options_class):
 from inspect import Parameter
 params = []
 for name in arg_names:
 params.append(Parameter(name, Parameter.POSITIONAL_ONLY))
 for name in var_arg_names:
 params.append(Parameter(name, Parameter.VAR_POSITIONAL))
 if options_class is not None:
 options_sig = inspect.signature(options_class)
 for p in options_sig.parameters.values():
 assert p.kind in (Parameter.POSITIONAL_OR_KEYWORD,
 Parameter.KEYWORD_ONLY)
 if var_arg_names:
 # Cannot have a positional argument after a *args
 p = p.replace(kind=Parameter.KEYWORD_ONLY)
 params.append(p)
 params.append(Parameter("options", Parameter.KEYWORD_ONLY,
 default=None))
 params.append(Parameter("memory_pool", Parameter.KEYWORD_ONLY,
 default=None))
 return inspect.Signature(params)


def _wrap_function(name, func):
 options_class = _get_options_class(func)
 arg_names = _get_arg_names(func)
 has_vararg = arg_names and arg_names[-1].startswith('*')
 if has_vararg:
 var_arg_names = [arg_names.pop().lstrip('*')]
 else:
 var_arg_names = []

 wrapper = _make_generic_wrapper(
 name, func, options_class, arity=func.arity)
 wrapper.__signature__ = _make_signature(arg_names, var_arg_names,
 options_class)
 return _decorate_compute_function(wrapper, name, func, options_class)


def _make_global_functions():
 """
 Make global functions wrapping each compute function.

 Note that some of the automatically-generated wrappers may be overridden
 by custom versions below.
 """
 g = globals()
 reg = function_registry()

 # Avoid clashes with Python keywords
 rewrites = {'and': 'and_',
 'or': 'or_'}

 for cpp_name in reg.list_functions():
 name = rewrites.get(cpp_name, cpp_name)
 func = reg.get_function(cpp_name)
 if func.kind == "hash_aggregate":
 # Hash aggregate functions are not callable,
 # so let's not expose them at module level.
 continue
 if func.kind == "scalar_aggregate" and func.arity == 0:
 # Nullary scalar aggregate functions are not callable
 # directly so let's not expose them at module level.
 continue
 assert name not in g, name
 g[cpp_name] = g[name] = _wrap_function(name, func)


_make_global_functions()
# Alias for consistency; globals() is needed to avoid Python lint errors
utf8_zfill = utf8_zero_fill = globals()["utf8_zero_fill"]


def cast(arr, target_type=None, safe=None, options=None, memory_pool=None):
 """
 Cast array values to another data type. Can also be invoked as an array
 instance method.

 Parameters
 ----------
 arr : Array-like
 target_type : DataType or str
 Type to cast to
 safe : bool, default True
 Check for overflows or other unsafe conversions
 options : CastOptions, default None
 Additional checks pass by CastOptions
 memory_pool : MemoryPool, optional
 memory pool to use for allocations during function execution.

 Examples
 --------
 >>> from datetime import datetime
 >>> import pyarrow as pa
 >>> arr = pa.array([datetime(2010, 1, 1), datetime(2015, 1, 1)])
 >>> arr.type
 TimestampType(timestamp[us])

 You can use ``pyarrow.DataType`` objects to specify the target type:

 >>> cast(arr, pa.timestamp('ms'))
 <pyarrow.lib.TimestampArray object at ...>
 [
 2010-01-01 00:00:00.000,
 2015-01-01 00:00:00.000
 ]

 >>> cast(arr, pa.timestamp('ms')).type
 TimestampType(timestamp[ms])

 Alternatively, it is also supported to use the string aliases for these
 types:

 >>> arr.cast('timestamp[ms]')
 <pyarrow.lib.TimestampArray object at ...>
 [
 2010-01-01 00:00:00.000,
 2015-01-01 00:00:00.000
 ]
 >>> arr.cast('timestamp[ms]').type
 TimestampType(timestamp[ms])

 Returns
 -------
 casted : Array
 The cast result as a new Array
 """
 safe_vars_passed = (safe is not None) or (target_type is not None)

 if safe_vars_passed and (options is not None):
 raise ValueError("Must either pass values for 'target_type' and 'safe'"
 " or pass a value for 'options'")

 if options is None:
 target_type = pa.types.lib.ensure_type(target_type)
 if safe is False:
 options = CastOptions.unsafe(target_type)
 else:
 options = CastOptions.safe(target_type)
 return call_function("cast", [arr], options, memory_pool)


def index(data, value, start=None, end=None, *, memory_pool=None):
 """
 Find the index of the first occurrence of a given value.

 Parameters
 ----------
 data : Array-like
 value : Scalar-like object
 The value to search for.
 start : int, optional
 end : int, optional
 memory_pool : MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.

 Returns
 -------
 index : int
 the index, or -1 if not found

 Examples
 --------
 >>> import pyarrow as pa
 >>> import pyarrow.compute as pc
 >>> arr = pa.array(["Lorem", "ipsum", "dolor", "sit", "Lorem", "ipsum"])
 >>> pc.index(arr, "ipsum")
 <pyarrow.Int64Scalar: 1>
 >>> pc.index(arr, "ipsum", start=2)
 <pyarrow.Int64Scalar: 5>
 >>> pc.index(arr, "amet")
 <pyarrow.Int64Scalar: -1>
 """
 if start is not None:
 if end is not None:
 data = data.slice(start, end - start)
 else:
 data = data.slice(start)
 elif end is not None:
 data = data.slice(0, end)

 if not isinstance(value, pa.Scalar):
 value = pa.scalar(value, type=data.type)
 elif data.type != value.type:
 value = pa.scalar(value.as_py(), type=data.type)
 options = IndexOptions(value=value)
 result = call_function('index', [data], options, memory_pool)
 if start is not None and result.as_py() >= 0:
 result = pa.scalar(result.as_py() + start, type=pa.int64())
 return result


def take(data, indices, *, boundscheck=True, memory_pool=None):
 """
 Select values (or records) from array- or table-like data given integer
 selection indices.

 The result will be of the same type(s) as the input, with elements taken
 from the input array (or record batch / table fields) at the given
 indices. If an index is null then the corresponding value in the output
 will be null.

 Parameters
 ----------
 data : Array, ChunkedArray, RecordBatch, or Table
 indices : Array, ChunkedArray
 Must be of integer type
 boundscheck : boolean, default True
 Whether to boundscheck the indices. If False and there is an out of
 bounds index, will likely cause the process to crash.
 memory_pool : MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.

 Returns
 -------
 result : depends on inputs
 Selected values for the given indices

 Examples
 --------
 >>> import pyarrow as pa
 >>> arr = pa.array(["a", "b", "c", None, "e", "f"])
 >>> indices = pa.array([0, None, 4, 3])
 >>> arr.take(indices)
 <pyarrow.lib.StringArray object at ...>
 [
 "a",
 null,
 "e",
 null
 ]
 """
 options = TakeOptions(boundscheck=boundscheck)
 return call_function('take', [data, indices], options, memory_pool)


def fill_null(values, fill_value):
 """Replace each null element in values with a corresponding
 element from fill_value.

 If fill_value is scalar-like, then every null element in values
 will be replaced with fill_value. If fill_value is array-like,
 then the i-th element in values will be replaced with the i-th
 element in fill_value.

 The fill_value's type must be the same as that of values, or it
 must be able to be implicitly casted to the array's type.

 This is an alias for :func:`coalesce`.

 Parameters
 ----------
 values : Array, ChunkedArray, or Scalar-like object
 Each null element is replaced with the corresponding value
 from fill_value.
 fill_value : Array, ChunkedArray, or Scalar-like object
 If not same type as values, will attempt to cast.

 Returns
 -------
 result : depends on inputs
 Values with all null elements replaced

 Examples
 --------
 >>> import pyarrow as pa
 >>> arr = pa.array([1, 2, None, 3], type=pa.int8())
 >>> fill_value = pa.scalar(5, type=pa.int8())
 >>> arr.fill_null(fill_value)
 <pyarrow.lib.Int8Array object at ...>
 [
 1,
 2,
 5,
 3
 ]
 >>> arr = pa.array([1, 2, None, 4, None])
 >>> arr.fill_null(pa.array([10, 20, 30, 40, 50]))
 <pyarrow.lib.Int64Array object at ...>
 [
 1,
 2,
 30,
 4,
 50
 ]
 """
 if not isinstance(fill_value, (pa.Array, pa.ChunkedArray, pa.Scalar)):
 fill_value = pa.scalar(fill_value, type=values.type)
 elif values.type != fill_value.type:
 fill_value = pa.scalar(fill_value.as_py(), type=values.type)

 return call_function("coalesce", [values, fill_value])


def top_k_unstable(
 values, k, sort_keys=None, null_placements=None, *, memory_pool=None):
 """
 Select the indices of the top-k ordered elements from array- or table-like
 data.

 This is a specialization for :func:`select_k_unstable`. Output is not
 guaranteed to be stable.

 Parameters
 ----------
 values : Array, ChunkedArray, RecordBatch, or Table
 Data to sort and get top indices from.
 k : int
 The number of `k` elements to keep.
 sort_keys : List-like
 Column key names to order by when input is table-like data.
 null_placements : A list of "at_start" or "at_end"
 Whether nulls and NaNs are placed at the start or at the end.
 Accepted values are "at_end", "at_start".
 memory_pool : MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.

 Returns
 -------
 result : Array
 Indices of the top-k ordered elements

 Examples
 --------
 >>> import pyarrow as pa
 >>> import pyarrow.compute as pc
 >>> arr = pa.array(["a", "b", "c", None, "e", "f"])
 >>> pc.top_k_unstable(arr, k=3)
 <pyarrow.lib.UInt64Array object at ...>
 [
 5,
 4,
 2
 ]
 """
 if sort_keys is None:
 sort_keys = []
 if isinstance(values, (pa.Array, pa.ChunkedArray)):
 sort_keys.append(("dummy", "descending", "at_end"))
 else:
 sort_keys = [(sort_key, "descending", null_placement)
 for sort_key, null_placement in zip(sort_keys, null_placements)]
 options = SelectKOptions(k, sort_keys)
 return call_function("select_k_unstable", [values], options, memory_pool)


def bottom_k_unstable(
 values, k, sort_keys=None, null_placements=None, *, memory_pool=None):
 """
 Select the indices of the bottom-k ordered elements from
 array- or table-like data.

 This is a specialization for :func:`select_k_unstable`. Output is not
 guaranteed to be stable.

 Parameters
 ----------
 values : Array, ChunkedArray, RecordBatch, or Table
 Data to sort and get bottom indices from.
 k : int
 The number of `k` elements to keep.
 sort_keys : List-like
 Column key names to order by when input is table-like data.
 null_placements : A list of "at_start" or "at_end"
 Whether nulls and NaNs are placed at the start or at the end.
 Accepted values are "at_end", "at_start".
 memory_pool : MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.

 Returns
 -------
 result : Array of indices
 Indices of the bottom-k ordered elements

 Examples
 --------
 >>> import pyarrow as pa
 >>> import pyarrow.compute as pc
 >>> arr = pa.array(["a", "b", "c", None, "e", "f"])
 >>> pc.bottom_k_unstable(arr, k=3)
 <pyarrow.lib.UInt64Array object at ...>
 [
 0,
 1,
 2
 ]
 """
 if sort_keys is None:
 sort_keys = []
 if isinstance(values, (pa.Array, pa.ChunkedArray)):
 sort_keys.append(("dummy", "ascending", "at_end"))
 else:
 sort_keys = [(sort_key, "ascending", null_placement)
 for sort_key, null_placement in zip(sort_keys, null_placements)]

 options = SelectKOptions(k, sort_keys)
 return call_function("select_k_unstable", [values], options, memory_pool)


def random(n, *, initializer='system', options=None, memory_pool=None):
 """
 Generate numbers in the range [0, 1).

 Generated values are uniformly-distributed, double-precision
 in range [0, 1). Algorithm and seed can be changed via RandomOptions.

 Parameters
 ----------
 n : int
 Number of values to generate, must be greater than or equal to 0
 initializer : int or str
 How to initialize the underlying random generator.
 If an integer is given, it is used as a seed.
 If "system" is given, the random generator is initialized with
 a system-specific source of (hopefully true) randomness.
 Other values are invalid.
 options : pyarrow.compute.RandomOptions, optional
 Alternative way of passing options.
 memory_pool : pyarrow.MemoryPool, optional
 If not passed, will allocate memory from the default memory pool.
 """
 options = RandomOptions(initializer=initializer)
 return call_function("random", [], options, memory_pool, length=n)


def field(*name_or_index):
 """Reference a column of the dataset.

 Stores only the field's name. Type and other information is known only when
 the expression is bound to a dataset having an explicit scheme.

 Nested references are allowed by passing multiple names or a tuple of
 names. For example ``('foo', 'bar')`` references the field named "bar"
 inside the field named "foo".

 Parameters
 ----------
 *name_or_index : string, multiple strings, tuple or int
 The name or index of the (possibly nested) field the expression
 references to.

 Returns
 -------
 field_expr : Expression
 Reference to the given field

 Examples
 --------
 >>> import pyarrow.compute as pc
 >>> pc.field("a")
 <pyarrow.compute.Expression a>
 >>> pc.field(1)
 <pyarrow.compute.Expression FieldPath(1)>
 >>> pc.field(("a", "b"))
 <pyarrow.compute.Expression FieldRef.Nested(FieldRef.Name(a) ...
 >>> pc.field("a", "b")
 <pyarrow.compute.Expression FieldRef.Nested(FieldRef.Name(a) ...
 """
 n = len(name_or_index)
 if n == 1:
 if isinstance(name_or_index[0], (str, int)):
 return Expression._field(name_or_index[0])
 elif isinstance(name_or_index[0], tuple):
 return Expression._nested_field(name_or_index[0])
 else:
 raise TypeError(
 "field reference should be str, multiple str, tuple or "
 f"integer, got {type(name_or_index[0])}"
 )
 # In case of multiple strings not supplied in a tuple
 else:
 return Expression._nested_field(name_or_index)


def scalar(value):
 """Expression representing a scalar value.

 Creates an Expression object representing a scalar value that can be used
 in compute expressions and predicates.

 Parameters
 ----------
 value : bool, int, float or string
 Python value of the scalar. This function accepts any value that can be
 converted to a ``pyarrow.Scalar`` using ``pa.scalar()``.

 Notes
 -----
 This function differs from ``pyarrow.scalar()`` in the following way:

 * ``pyarrow.scalar()`` creates a ``pyarrow.Scalar`` object that represents
 a single value in Arrow's memory model.
 * ``pyarrow.compute.scalar()`` creates an ``Expression`` object representing
 a scalar value that can be used in compute expressions, predicates, and
 dataset filtering operations.

 Returns
 -------
 scalar_expr : Expression
 An Expression representing the scalar value
 """
 return Expression._scalar(value)