ch!PSSKJr SSKJrJrJrJr SSKrSSK J r "SS5r g)) annotations)AnyIterableOptionalSequenceN)_PyArrowColumnc\rSrSrSrSSSjjrSSSjjr\SSj5rSSjr SSjr SSjr SS jr SS jr SS jrSS jrSS jrSSjrSSSjjrSrg)_PyArrowDataFramea  A data frame class, with only the methods required by the interchange protocol defined. A "data frame" represents an ordered collection of named columns. A column's "name" must be a unique string. Columns may be accessed by name or by position. This could be a public data frame class, or an object with the methods and attributes defined on this DataFrame class could be returned from the ``__dataframe__`` method of a public data frame class in a library adhering to the dataframe interchange protocol specification. cHXlUSLa [S5eX lX0lg)z Constructor - an instance of this (private) class is returned from `pa.Table.__dataframe__` or `pa.RecordBatch.__dataframe__`. TzKnan_as_null=True currently has no effect, use the default nan_as_null=FalseN)_df RuntimeError _nan_as_null _allow_copy)selfdf nan_as_null allow_copys pC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\pyarrow/interchange/dataframe.py__init___PyArrowDataFrame.__init__.s3 $ 4 (%c.[URX5$)aA Construct a new exchange object, potentially changing the parameters. ``nan_as_null`` is a keyword intended for the consumer to tell the producer to overwrite null values in the data with ``NaN``. It is intended for cases where the consumer does not support the bit mask or byte mask that is the producer's native representation. ``allow_copy`` is a keyword that defines whether or not the library is allowed to make a copy of the data. For example, copying data would be necessary if a library supports strided buffers, given that this protocol specifies contiguous buffers. )r r )rrrs r __dataframe___PyArrowDataFrame.__dataframe__Cs!;CCrc(URRR(afURRRR5VVs0sH)upSUR S5-UR S5_M+ nnnU$0$s snnf)a The metadata for the data frame, as a dictionary with string keys. The contents of `metadata` may be anything, they are meant for a library to store information that it needs to, e.g., roundtrip losslessly or for two implementations to share data that is not (yet) part of the interchange protocol specification. For avoiding collisions with other entries, please add name the keys with the name of the library followed by a period and the desired name, e.g, ``pandas.indexcol``. zpyarrow.utf8)r schemametadataitemsdecode)rkvschema_metadatas rr_PyArrowDataFrame.metadataSs} 88?? # #+/88??+C+C+I+I+KM+K41 *AHHV,<NN+K M" "I Ms0Bc.URR$)z0 Return the number of columns in the DataFrame. )r num_columnsrs rr'_PyArrowDataFrame.num_columnsgsxx###rc.URR$)z; Return the number of rows in the DataFrame, if available. )r num_rowsr(s rr+_PyArrowDataFrame.num_rowsmsxx   rc[UR[R5(agURR 5n[ U5$)z8 Return the number of chunks the DataFrame consists of. ) isinstancer pa RecordBatch to_batcheslen)rbatchess r num_chunks_PyArrowDataFrame.num_chunksss: dhh / / hh))+Gw< rcBURRR$)z/ Return an iterator yielding the column names. )r rnamesr(s r column_names_PyArrowDataFrame.column_namessxx$$$rc\[URRU5URS9$)z. Return the column at the indicated position. rrr columnr)ris r get_column_PyArrowDataFrame.get_columns)dhhooa0)-)9)9; ;rc\[URRU5URS9$)z5 Return the column whose name is the indicated name. r<r=)rnames rget_column_by_name$_PyArrowDataFrame.get_column_by_names)dhhood3)-)9)9; ;rc|URRVs/sHn[XRS9PM sn$s snf)z* Return an iterator yielding the columns. r<)r columnsrr)rcols r get_columns_PyArrowDataFrame.get_columnss> xx'' ' 3+;+; <'   s9c[URR[U55URUR 5$)zC Create a new DataFrame by selecting a subset of columns by index. r r selectlistrr)rindicess rselect_columns _PyArrowDataFrame.select_columnss5! HHOODM *D,=,=t?O?O  rc[URR[U55URUR 5$)zB Create a new DataFrame by selecting a subset of columns by name. rL)rr8s rselect_columns_by_name(_PyArrowDataFrame.select_columns_by_names5 ! HHOODK ($*;*;T=M=M  rNc2U(GaUS:GaUR5U-nUR5U-S:waUS- n[UR[R5(aURR US9nOB/n[ SX!-U5H-nURURRXB55 M/ [U5US- :Xa9UR[R"//URRS95 OQ[UR[R5(aURR 5nO UR/nUVs/sH$n[UURUR5PM& nnU$s snf)aQ Return an iterator yielding the chunks. By default (None), yields the chunks that the data is stored as by the producer. If given, ``n_chunks`` must be a multiple of ``self.num_chunks()``, meaning the producer must subdivide each chunk before yielding it. Note that the producer must ensure that all columns are chunked the same way. r.r) max_chunksize)r)r+r/r r0Tabler2rangeappendslicer3 record_batchrr rr)rn_chunks chunk_sizer4startbatchiterators r get_chunks_PyArrowDataFrame.get_chunkssM 1 H4J}})Q.a $((BHH--((--J-G"1j&;ZHENN488>>%#DEI7|x!|+rtDHHOOLM$((BHH--((--/88* ")*")&e&*&7&7&*&6&68") * *s%+F)rr r)FT)rzpa.Table | pa.RecordBatchrboolrrcreturnNone)rrcrrcrdr )rdzdict[str, Any])rdint)rdz Iterable[str])r?rfrdr)rCstrrdr)rdzIterable[_PyArrowColumn])rOz Sequence[int]rdr )r8z Sequence[str]rdr )N)r\z Optional[int]rdzIterable[_PyArrowDataFrame])__name__ __module__ __qualname____firstlineno____doc__rrpropertyrr'r+r5r9r@rDrIrPrSra__static_attributes__rrr r s  "&+&&&  &,=ADD59D D &$ !  % ;;   "  )-)%) $))rr ) __future__rtypingrrrrpyarrowr0pyarrow.interchange.columnrr rorrrts($#5zzr