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F ii&?&?@A ii&>&>?@ '' 6 ''..0A66i=='4466 66II9#9#9I: MM!  1  i 9+A+A(,. / MM)M9+A+A$(*+   V $$rVct|}t|}|xr|djd}|r!|jj dg}ng}t ||||j }t||||_t||||S)N*)rb) rrU startswithpoplstriprrbr __signature__r)rarSr]rR has_varargrrs rT_wrap_functionr#s&t,Mt$I>> from datetime import datetime >>> import pyarrow as pa >>> arr = pa.array([datetime(2010, 1, 1), datetime(2015, 1, 1)]) >>> arr.type TimestampType(timestamp[us]) You can use ``pyarrow.DataType`` objects to specify the target type: >>> cast(arr, pa.timestamp('ms')) [ 2010-01-01 00:00:00.000, 2015-01-01 00:00:00.000 ] >>> cast(arr, pa.timestamp('ms')).type TimestampType(timestamp[ms]) Alternatively, it is also supported to use the string aliases for these types: >>> arr.cast('timestamp[ms]') [ 2010-01-01 00:00:00.000, 2015-01-01 00:00:00.000 ] >>> arr.cast('timestamp[ms]').type TimestampType(timestamp[ms]) Returns ------- casted : Array The cast result as a new Array NzRMust either pass values for 'target_type' and 'safe' or pass a value for 'options'Fcast) ValueErrorpatypeslib ensure_typerunsafesafer@)arr target_typerrrsafe_vars_passeds rTrrSslD(Fk.EW0:; ;hhll..{; 5=!((5G!&&{3G # ==rVrcb|*||j|||z }n&|j|}n||jd|}t|tjs"tj||j }nH|j |j k7r/tj|j |j }t|}td|g||}|M|j dk\r:tj|j |ztj}|S)a Find the index of the first occurrence of a given value. Parameters ---------- data : Array-like value : Scalar-like object The value to search for. start : int, optional end : int, optional memory_pool : MemoryPool, optional If not passed, will allocate memory from the default memory pool. Returns ------- index : int the index, or -1 if not found rrvvalueindex) slicerrScalarscalarrvas_pyrr@int64)datarstartendrrresults rTrrs&  ?::eS5[1D::e$D zz!S! eRYY ' %dii0 ejj  %++-dii8'G 7TFG[ AF V\\^q06<<>E1 C MrVT) boundscheckrc:t|}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)r:r@)rindicesrrrs rTrrs$Pk2G $'; GGrVcdt|tjtjtjfs"tj ||j }nH|j |j k7r/tj |j|j }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 ChunkedArrayrrrvrr@)r~ fill_values rT fill_nullrsuf j288R__bii"H IYYz <   'YYz//1 D fj%9 ::rVc|g}t|tjtjfr|j dn t d|}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 ] )dummy descendingc |dfS)Nrrkey_names rTz top_k_unstable..Os (L)ArVselect_k_unstablerrrrrqmapr1r@r~k sort_keysrrs rTtop_k_unstabler%scJ &288R__5601A9M Q *G ,vh MMrVc|g}t|tjtjfr|j dn t d|}t ||}td|g||S)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 ] )r ascendingc |dfS)Nrrrs rTrz#bottom_k_unstable..~s (K)@rVrrrs rTbottom_k_unstablerTscJ &288R__56/0@)L Q *G ,vh MMrVsystem) initializerrrc:t|}tdg|||S)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@)nrrrs rTrrs!, 4G 2w A FFrVc8t|}|dk(rvt|dttfrt j |dSt|dt rt j|dStdt|dt j|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@rArBrCrDrErFrGrHrIrJ collectionsrKr{textwraprLrxpyarrowrrMpyarrow.vendoredrNrUrWr_rrrrrrrrrrrrrrr rrrVrTrs7$MMMMMMMMMMMMMMMMMMM^#'&0+>/Pf *>%. J ;:B>J#D#L(,)HX8;v,NT,N^,N,N^&tG4.7b%rV