# 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, 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() 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')) [ 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]') [ 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 """ 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) [ "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) [ 1, 2, 5, 3 ] >>> arr = pa.array([1, 2, None, 4, None]) >>> arr.fill_null(pa.array([10, 20, 30, 40, 50])) [ 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) [ 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) [ 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) 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") >>> pc.field(1) >>> pc.field(("a", "b")) >> pc.field("a", "b")