*j9dZddlmZddlmZmZddlmZmZddl m Z ddl m Z e rddl ZddlmZddlmZerdd lmZdd lmZmZGd d e ZGddeZGddeZy)z Accessors for arrow-backed data.) annotations)ABCMetaabstractmethod) TYPE_CHECKINGcast) HAS_PYARROW) is_list_likeN) ArrowDtype)Iterator) DataFrameSeriescHeZdZeddZeddZddZedZy) ArrowAccessorcB||_||_|j|yN)_data_validation_msg _validate)selfdatavalidation_msgs DC:\FundamentalWorkbench\vendor\pandas\core\arrays\arrow\accessors.py__init__zArrowAccessor.__init__"s - tcyrr pyarrow_dtypes r_is_valid_pyarrow_dtypez%ArrowAccessor._is_valid_pyarrow_dtype(s rc|j}trt|ts%t |j j ||j|js%t |j j |y)N)dtype) r!r isinstancer AttributeErrorrformatrr)rrr!s rrzArrowAccessor._validate,so *UJ"? !5!5!>> import pyarrow as pa >>> s = pd.Series( ... [ ... [1, 2, 3], ... [3], ... ], ... dtype=pd.ArrowDtype(pa.list_(pa.int64())), ... ) >>> s.list.len() 0 3 1 1 dtype: int32[pyarrow] rr r!indexname) pandasr pclist_value_lengthr'r typerrErF)rr value_lengthss rlenzListAccessor.lenSsQ< ",,T^^<  ]//0**""   rcddlm}t|trgt j |j |}||t|j|jj|jjSt|tr|j|j|j}}}|d}|d}t j |j |||}||t|j|jj|jjSt#dt|j$)a Index or slice lists in the Series. Parameters ---------- key : int | slice Index or slice of indices to access from each list. Returns ------- pandas.Series The list at requested index. See Also -------- ListAccessor.flatten : Flatten list values. Examples -------- >>> import pyarrow as pa >>> s = pd.Series( ... [ ... [1, 2, 3], ... [3], ... ], ... dtype=pd.ArrowDtype(pa.list_(pa.int64())), ... ) >>> s.list[0] 0 1 1 3 dtype: int64[pyarrow] rrCrDz!key must be an int or slice, got )rGr r"intrH list_elementr'r rJrrErFslicestartstopstep list_slice ValueErrorr/)rkeyr elementrRrSrTsliceds r __getitem__zListAccessor.__getitem__{sB " c3  oodnnc:G .jj&&ZZ__   U #!$ 388SXX4E}|]]4>>5$EF -jj&&ZZ__  @cASAS@TUV VrcFtdt|jd)N'z' object is not iterable) TypeErrorrJr/r(s r__iter__zListAccessor.__iter__s"!DJ//00HIJJrcddlm}tjj |j }tjj |j }|jjj|jtjd|j}||t|j||jjS)a Flatten list values. Returns ------- pandas.Series The data from all lists in the series flattened. See Also -------- ListAccessor.__getitem__ : Index or slice values in the Series. Examples -------- >>> import pyarrow as pa >>> s = pd.Series( ... [ ... [1, 2, 3], ... [3], ... ], ... dtype=pd.ArrowDtype(pa.list_(pa.int64())), ... ) >>> s.list.flatten() 0 1 0 2 0 3 1 3 dtype: int64[pyarrow] rrCrD)rGr r=computerIr' list_flattenrrErepeat fill_nullscalarrJr rF)rr counts flattenedrEs rflattenzListAccessor.flattens< "--dnn=JJ++DNN;    ''(8(81fkk9R(ST Y^^,   rrr.r,r*r )rWz int | slicer*r )r*r ) r/r0r1__doc__rrrLrZr^rg __classcell__r;s@rr5r5;s+  & PBWHK( rr5cXeZdZdZddfd Zd dZed dZ d dZd dZ xZ S) StructAccessorz Accessor object for structured data properties of the Series values. Parameters ---------- data : Series Series containing Arrow struct data. c(t||dy)NzNCan only use the '.struct' accessor with 'struct[pyarrow]' dtype, not {dtype}.r7r8r:s rrzStructAccessor.__init__s  &  rc@tjj|Sr)r=r> is_structrs rrz&StructAccessor._is_valid_pyarrow_dtypesxx!!-00rcddlm}m}|jjj }|Dcgc]}t |j}}|Dcgc]}|j}}||||Scc}wcc}w)aZ Return the dtype object of each child field of the struct. Returns ------- pandas.Series The data type of each child field. See Also -------- Series.dtype: Return the dtype object of the underlying data. Examples -------- >>> import pyarrow as pa >>> s = pd.Series( ... [ ... {"version": 1, "project": "pandas"}, ... {"version": 2, "project": "pandas"}, ... {"version": 1, "project": "numpy"}, ... ], ... dtype=pd.ArrowDtype( ... pa.struct([("version", pa.int64()), ("project", pa.string())]) ... ), ... ) >>> s.struct.dtypes version int64[pyarrow] project string[pyarrow] dtype: object r)Indexr )rE) rGrrr rr!rr rJrF)rrrr pa_typestructr>namess rdtypeszStructAccessor.dtypessn@ **""007>?wVFKK(w?+23773e5<00@3s A4A9cddlm} dfd |jjj}||}t j ||}||t|j|jj|S)a Extract a child field of a struct as a Series. Parameters ---------- name_or_index : str | bytes | int | expression | list Name or index of the child field to extract. For list-like inputs, this will index into a nested struct. Returns ------- pandas.Series The data corresponding to the selected child field. See Also -------- Series.struct.explode : Return all child fields as a DataFrame. Notes ----- The name of the resulting Series will be set using the following rules: - For string, bytes, or integer `name_or_index` (or a list of these, for a nested selection), the Series name is set to the selected field's name. - For a :class:`pyarrow.compute.Expression`, this is set to the string form of the expression. - For list-like `name_or_index`, the name will be set to the name of the final field selected. Examples -------- >>> import pyarrow as pa >>> s = pd.Series( ... [ ... {"version": 1, "project": "pandas"}, ... {"version": 2, "project": "pandas"}, ... {"version": 1, "project": "numpy"}, ... ], ... dtype=pd.ArrowDtype( ... pa.struct([("version", pa.int64()), ("project", pa.string())]) ... ), ... ) Extract by field name. >>> s.struct.field("project") 0 pandas 1 pandas 2 numpy Name: project, dtype: string[pyarrow] Extract by field index. >>> s.struct.field(0) 0 1 1 2 2 1 Name: version, dtype: int64[pyarrow] Or an expression >>> import pyarrow.compute as pc >>> s.struct.field(pc.field("project")) 0 pandas 1 pandas 2 numpy Name: project, dtype: string[pyarrow] For nested struct types, you can pass a list of values to index multiple levels: >>> version_type = pa.struct( ... [ ... ("major", pa.int64()), ... ("minor", pa.int64()), ... ] ... ) >>> s = pd.Series( ... [ ... {"version": {"major": 1, "minor": 5}, "project": "pandas"}, ... {"version": {"major": 2, "minor": 1}, "project": "pandas"}, ... {"version": {"major": 1, "minor": 26}, "project": "numpy"}, ... ], ... dtype=pd.ArrowDtype( ... pa.struct([("version", version_type), ("project", pa.string())]) ... ), ... ) >>> s.struct.field(["version", "minor"]) 0 5 1 1 2 26 Name: minor, dtype: int64[pyarrow] >>> s.struct.field([0, 0]) 0 1 1 2 2 1 Name: major, dtype: int64[pyarrow] rrCc,t|tr'|jj|j}|St|t t fr|}|St|tjr t |}|St|rtt|}|}|rltt|}|j}||}|jj|jj|}|j}|rlStd)NzVname_or_index must be an int, str, bytes, pyarrow.compute.Expression, or list of those)r"rOrJfieldrFr)bytesrH Expressionr listreversedrpopget_field_indexrV)level_name_or_indexrrFselected name_or_indexget_names rrz&StructAccessor.field..get_names-s3yy':;@@0K//#u>*,K+/?./(K'12'+84G+H&I#)+/t5H*I'$7$;$;$=M#M8>> import pyarrow as pa >>> s = pd.Series( ... [ ... {"version": 1, "project": "pandas"}, ... {"version": 2, "project": "pandas"}, ... {"version": 1, "project": "numpy"}, ... ], ... dtype=pd.ArrowDtype( ... pa.struct([("version", pa.int64()), ("project", pa.string())]) ... ), ... ) >>> s.struct.explode() version project 0 1 pandas 1 2 pandas 2 1 numpy r)concatcolumns)axis)rGrr'rJrange num_fieldsry)rrrsis rexplodezStructAccessor.explodesPB "..%%$)'*<*<$= >$=qTZZ]$= >Y  >sArr.r,rh)rrr*r )r*r ) r/r0r1rirrr2rvryrrjrks@rrmrmsI 1'1'1R` `  ` D& rrm)ri __future__rabcrrtypingrr pandas.compatrpandas.core.dtypes.commonr pyarrowr=pyarrow.computer`rHpandas.core.dtypes.dtypesr collections.abcr rGr r rr5rmrrrrs]&" &2 4( *g*4o =o dH ]H r