o #ifuZ@s<ddlmZmZmZmZmZmZmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCmDZDmEZEmFZFmGZGddlHmIZIddlJZJddlKmLZLddlMZMddlNZOddlNmPZPddlQmRZRddZSeId d ZTd d ZUd dZVddZWddZXddZYddZZddZ[ddZ\e\d2ddZ]d3ddddZ^d dd!d"d#Z_d$d%Z`d4ddd&d'Zad4ddd(d)Zbd*ddd+d,d-Zcd.d/Zdd0d1ZedS)5)GFunctionFunctionOptionsFunctionRegistryHashAggregateFunctionHashAggregateKernelKernelScalarAggregateFunctionScalarAggregateKernelScalarFunction ScalarKernelVectorFunction VectorKernelArraySortOptionsAssumeTimezoneOptions CastOptions CountOptionsCumulativeOptionsCumulativeSumOptionsDayOfWeekOptionsDictionaryEncodeOptionsRunEndEncodeOptionsElementWiseAggregateOptionsExtractRegexOptions FilterOptions IndexOptions JoinOptionsListSliceOptionsMakeStructOptionsMapLookupOptionsMatchSubstringOptions ModeOptions NullOptions PadOptionsPairwiseOptionsPartitionNthOptionsQuantileOptions RandomOptions RankOptionsReplaceSliceOptionsReplaceSubstringOptionsRoundBinaryOptions RoundOptionsRoundTemporalOptionsRoundToMultipleOptionsScalarAggregateOptionsSelectKOptionsSetLookupOptions SliceOptions SortOptions SplitOptionsSplitPatternOptionsStrftimeOptionsStrptimeOptionsStructFieldOptions TakeOptionsTDigestOptions TrimOptionsUtf8NormalizeOptionsVarianceOptions WeekOptions call_functionfunction_registry get_functionlist_functionscall_tabular_functionregister_scalar_functionregister_tabular_functionregister_aggregate_functionregister_vector_function UdfContext Expression) namedtupleN)dedent)_compute_docstrings) docscrapecCs|jjSN)_doc arg_names)funcrQH/var/www/html/corbot_env/lib/python3.10/site-packages/pyarrow/compute.py_get_arg_namesjsrS_OptionsClassDoc)paramscCs"|jsdSt|j}t|dS)N Parameters)__doc__rLNumpyDocStringrT) options_classdocrQrQrR_scrape_options_class_docqs  r[cCs|j}t|j|j|j|jd|_||_||_g}|j }|s-|jdkr$dnd}d |j|}| |d|j }|rB| |dt j|j} | tdt|} | D]} |jd vr`d } nd } | | d | d | dqV|durt|} | r| jD]}| |jd |jd |jD] }| d|d qqn%td|jdtt|}|jD]}| td |j|jq| td|jd| td| dur| d t| d d||_ |S)N)namearityrYoptions_required argumentsargumentz,Call compute function {!r} with the given {}z. z z. Parameters ---------- )vectorscalar_aggregatez Array-likezArray-like or scalar-likez :  z" Argument to compute function. z zOptions class z does not have a docstringz {0} : optional Parameter for {1} constructor. Either `options` or `{0}` can be passed, but not both at the same time. z& options : pyarrow.compute.zK, optional Alternative way of passing options. z memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the default memory pool. z {} )!rNdictr\r]rYr^__arrow_compute_function____name__ __qualname__summaryformatappend descriptionrKfunction_doc_additionsgetrJrSkindr[rUtypedescwarningswarnRuntimeWarninginspect signature parametersvaluesstripjoinrW)wrapper exposed_namerPrYcpp_doc doc_piecesrjarg_strrm doc_additionrOarg_namearg_typeoptions_class_docps options_sigrQrQrR_decorate_compute_functionxsl         rcCsD|jj}|sdSzt|WSty!td|tYdSw)Nz!Python binding for {} not exposed)rNrYglobalsKeyErrorrsrtrkru)rP class_namerQrQrR_get_options_classs  rcCst|s|r|durtd|||i|S|dur8t|tr&|di|St||r-|Std||t|dS)NzMFunction {!r} called with both an 'options' argument and additional argumentsz-Function {!r} expected a {} parameter, got {}rQ) TypeErrorrk isinstancerfrq)r\rYoptionsargskwargsrQrQrR_handle_optionss"  rcs@durddfdd }|Sdddfdd }|S)N memory_poolcsbturt|krtddt|d|r*t|dtr*tt|S|d|S)N takes  positional argument(s), but were givenr)EllipsislenrrrH_calllistcall)rr)r]rP func_namerQrRr|s z&_make_generic_wrapper..wrapper)rrcstur&t|krtddt|d|d}|d}nd}t|||}|rBt|dtrBtt||S|||S)NrrrrQr) rrrrrrHrrr)rrrr option_argsr]rPrrYrQrRr|s    rQ)rrPrYr]r|rQrrR_make_generic_wrappers rcCsddlm}g}|D] }||||jq |D] }||||jq|durYt|}|jD]}|j|j |j fvs?J|rH|j |j d}||q2||d|j dd||d|j ddt |S)Nr) Parameter)rpr)defaultr) rvrrlPOSITIONAL_ONLYVAR_POSITIONALrwrxryrpPOSITIONAL_OR_KEYWORD KEYWORD_ONLYreplace Signature)rO var_arg_namesrYrrUr\rrrQrQrR_make_signature s,       rcCsjt|}t|}|o|dd}|r|dg}ng}t||||jd}t||||_t ||||S)N*)r]) rrS startswithpoplstriprr]r __signature__r)r\rPrYrO has_varargrr|rQrQrR_wrap_function"s rcCst}t}ddd}|D]1}|||}||}|jdkr"q|jdkr-|jdkr-q||vs5J|t||||<||<qdS)z Make global functions wrapping each compute function. Note that some of the automatically-generated wrappers may be overridden by custom versions below. and_or_)andorhash_aggregatercrN)rr?rAror@rpr]r)gregrewritescpp_namer\rPrQrQrR_make_global_functions2s    rcCsh|dup|du}|r|durtd|dur,tjj|}|dur't|}nt|}td|g||S)a 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 NzRMust either pass values for 'target_type' and 'safe' or pass a value for 'options'Fcast) ValueErrorpatypeslib ensure_typerunsafesafer>)arr target_typerrrsafe_vars_passedrQrQrRrRs6   rrcCs|dur|dur||||}n||}n |dur!|d|}t|tjs0tj||jd}n|j|jkr@tj||jd}t|d}td|g||}|durd|dkrdtj||t d}|S)a 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 Nrrqvalueindex) slicerrScalarscalarrqas_pyrr>int64)datarstartendrrresultrQrQrRrs     rT) boundscheckrcCst|d}td||g||S)a 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 ] )rtake)r8r>)rindicesrrrrQrQrRrs (rcCsVt|tjtjtjfstj||jd}n|j|jkr$tj||jd}td||gS)aeReplace 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 ] rcoalesce) rrArray ChunkedArrayrrrqrr>)ry fill_valuerQrQrR fill_nulls 3 rcCR|durg}t|tjtjfr|dntdd|}t||}td|g||S)a 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 ] N)dummy descendingcS|dfS)NrrQkey_namerQrQrRNz top_k_unstable..select_k_unstablerrrrrlmapr/r>ryk sort_keysrrrQrQrRtop_k_unstable$%  rcCr)a 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 ] N)r ascendingcSr)NrrQrrQrQrRr}rz#bottom_k_unstable..rrrrQrQrRbottom_k_unstableSrrsystem) initializerrrcCst|d}tdg|||dS)aB 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. )rrandom)length)r&r>)nrrrrQrQrRrs rcGslt|}|dkr1t|dttfrt|dSt|dtr&t|dStdt |dt|S)aReference 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") r?r@rArBrCrDrErFrGrH collectionsrIrvtextwraprJrspyarrowrrKpyarrow.vendoredrLrSrTr[rrrrrrrrrrrrrrrrrQrQrQrRs:& N    S  E&,;// 1