# 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
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# 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,
FilterOptions,
IndexOptions,
JoinOptions,
ListSliceOptions,
ListFlattenOptions,
MakeStructOptions,
MapLookupOptions,
MatchSubstringOptions,
ModeOptions,
NullOptions,
PadOptions,
PairwiseOptions,
PartitionNthOptions,
QuantileOptions,
RandomOptions,
RankOptions,
ReplaceSliceOptions,
ReplaceSubstringOptions,
RoundBinaryOptions,
RoundOptions,
RoundTemporalOptions,
RoundToMultipleOptions,
ScalarAggregateOptions,
SelectKOptions,
SetLookupOptions,
SliceOptions,
SortOptions,
SplitOptions,
SplitPatternOptions,
StrftimeOptions,
StrptimeOptions,
StructFieldOptions,
TakeOptions,
TDigestOptions,
TrimOptions,
Utf8NormalizeOptions,
VarianceOptions,
WeekOptions,
# 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 = ("Call compute function {!r} with the given {}"
.format(func.name, 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("""\
{0} : optional
Parameter for {1} constructor. Either `options`
or `{0}` can be passed, but not both at the same time.
""".format(p.name, options_class.__name__)))
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:
doc_pieces.append("\n{}\n".format(dedent(doc_addition).strip("\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("Python binding for {} not exposed"
.format(class_name), RuntimeWarning)
return None
def _handle_options(name, options_class, options, args, kwargs):
if args or kwargs:
if options is not None:
raise TypeError(
"Function {!r} called with both an 'options' argument "
"and additional arguments"
.format(name))
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(
"Function {!r} expected a {} parameter, got {}"
.format(name, options_class, 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()
[docs]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)
[docs]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
"""
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
[docs]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)
[docs]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, *, 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.
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"))
else:
sort_keys = map(lambda key_name: (key_name, "descending"), sort_keys)
options = SelectKOptions(k, sort_keys)
return call_function("select_k_unstable", [values], options, memory_pool)
def bottom_k_unstable(values, k, sort_keys=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.
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"))
else:
sort_keys = map(lambda key_name: (key_name, "ascending"), sort_keys)
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)
[docs]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)
[docs]def scalar(value):
"""Expression representing a scalar value.
Parameters
----------
value : bool, int, float or string
Python value of the scalar. Note that only a subset of types are
currently supported.
Returns
-------
scalar_expr : Expression
An Expression representing the scalar value
"""
return Expression._scalar(value)
