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F i&?&?@A i&>&>?@ '' 6 ''..0A66i=='4466 66II9#9#9I: MM!  1  i 9+A+A(,. / MM)M9+A+A$(*+   V $$r^c[U5n[U5nU=(a USRS5nU(a!UR5R S5/nO/n[ XX!R S9n[X5U5Ul[X`X5$)N*)rj) rr] startswithpoplstriprrjr __signature__r)rir[rerZ has_varargrrs r\_wrap_functionr*s&t,Mt$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_typerunsafesaferH)arr target_typerrrsafe_vars_passeds r\rr\slD(Fk.EW0:; ;hhll..{; 5=!((5G!&&{3G # ==r^rcbUb*UbURX#U- 5nO'URU5nOUbURSU5n[U[R5(d[R"XR S9nOGUR UR :wa-[R"UR 5UR S9n[US9n[SU/XT5nUbMUR 5S:a9[R"UR 5U-[R"5S9nU$)a Find the index of the first occurrence of a given value. Parameters ---------- data : Array-like value : Scalar-like object The value to search for. start : int, optional end : int, optional memory_pool : MemoryPool, optional If not passed, will allocate memory from the default memory pool. Returns ------- index : int the index, or -1 if not found Examples -------- >>> import pyarrow as pa >>> import pyarrow.compute as pc >>> arr = pa.array(["Lorem", "ipsum", "dolor", "sit", "Lorem", "ipsum"]) >>> pc.index(arr, "ipsum") >>> pc.index(arr, "ipsum", start=2) >>> pc.index(arr, "amet") rr}valueindex) slicerrScalarscalarr}as_pyrrHint64)datarstartendrrresults r\rrs>  ?::e5[1D::e$D zz!S! eRYY ' ' %ii0 ejj  %++-dii8'G 7TFG AF V\\^q06<<>E1 C Mr^T) boundscheckrc0[US9n[SX/XC5$)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@rH)rindicesrrrs r\rrs Pk2G $' GGr^cd[U[R[R[R45(d[R "XR S9nOGUR UR :wa-[R "UR5UR S9n[SX/5$)aReplace 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 ChunkedArrayrrr}rrH)r fill_values r\ fill_nullrstf j288R__bii"H I IYYz <   'YYz//1 D f%9 ::r^cUc/n[U[R[R45(aUR S5 O [ SU5n[ X5n[SU/XC5$)a< Select the indices of the top-k ordered elements from array- or table-like data. This is a specialization for :func:`select_k_unstable`. Output is not guaranteed to be stable. Parameters ---------- values : Array, ChunkedArray, RecordBatch, or Table Data to sort and get top indices from. k : int The number of `k` elements to keep. sort_keys : List-like Column key names to order by when input is table-like data. memory_pool : MemoryPool, optional If not passed, will allocate memory from the default memory pool. Returns ------- result : Array Indices of the top-k ordered elements Examples -------- >>> import pyarrow as pa >>> import pyarrow.compute as pc >>> arr = pa.array(["a", "b", "c", None, "e", "f"]) >>> pc.top_k_unstable(arr, k=3) [ 5, 4, 2 ] )dummy descendingc US4$)Nrrkey_names r\ top_k_unstable..ds (L)Ar^select_k_unstablerrrrrxmapr6rHrk sort_keysrrs r\top_k_unstabler:sbJ &288R__56601A9M Q*G ,vh MMr^cUc/n[U[R[R45(aUR S5 O [ SU5n[ X5n[SU/XC5$)aS Select the indices of the bottom-k ordered elements from array- or table-like data. This is a specialization for :func:`select_k_unstable`. Output is not guaranteed to be stable. Parameters ---------- values : Array, ChunkedArray, RecordBatch, or Table Data to sort and get bottom indices from. k : int The number of `k` elements to keep. sort_keys : List-like Column key names to order by when input is table-like data. memory_pool : MemoryPool, optional If not passed, will allocate memory from the default memory pool. Returns ------- result : Array of indices Indices of the bottom-k ordered elements Examples -------- >>> import pyarrow as pa >>> import pyarrow.compute as pc >>> arr = pa.array(["a", "b", "c", None, "e", "f"]) >>> pc.bottom_k_unstable(arr, k=3) [ 0, 1, 2 ] )r ascendingc US4$)Nrrrs r\r#bottom_k_unstable..s (K)@r^rrrs r\bottom_k_unstabler isbJ &288R__566/0@)L Q*G ,vh MMr^system) initializerrrc,[US9n[S/X#US9$)a Generate numbers in the range [0, 1). Generated values are uniformly-distributed, double-precision in range [0, 1). Algorithm and seed can be changed via RandomOptions. Parameters ---------- n : int Number of values to generate, must be greater than or equal to 0 initializer : int or str How to initialize the underlying random generator. If an integer is given, it is used as a seed. If "system" is given, the random generator is initialized with a system-specific source of (hopefully true) randomness. Other values are invalid. options : pyarrow.compute.RandomOptions, optional Alternative way of passing options. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the default memory pool. )r random)length)r+rH)nr rrs r\rrs, 4G 2wA FFr^cT[U5nUS:Xa[US[[45(a[R "US5$[US[ 5(a[R"US5$[S[US535e[R"U5$)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@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrR collectionsrSrtextwraprTrpyarrowrrUpyarrow.vendoredrVr]r_rgrrrrrrrr utf8_zfillrrrrrrr rrrrr^r\r"sU$UUUUUUUUUUUUUUUUUUUUUn#'&0+>/Pf (>%. J ;:%i(899 ^B>J/D/d(,)HX8;v,NT,N^,N,N^&tG4.7b%r^