A>iJSrSSKJr SSKJrJr SSKrSSKJr SSK J r SSK J r SSK Jr SS KJr SS KJr SS KJr SS KJr SS KJr \(aSSKJrJr SSKJrJrJrJ r J!r! SSSjjr"\"S5SSS\ RF4SSjj5r$g)zfeather-format compat) annotations) TYPE_CHECKINGAnyN)using_string_dtype)lib)import_optional_dependency)Pandas4Warning) set_module)check_dtype_backend) DataFrame)arrow_table_to_pandas) get_handle)HashableSequence) DtypeBackendFilePath ReadBufferStorageOptions WriteBufferc [S5 SSKJn [U[5(d [ S5e[ USUSS9nUR"XR40UD6 SSS5 g!,(df  g=f) a0 Write a DataFrame to the binary Feather format. Parameters ---------- df : DataFrame path : str, path object, or file-like object storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. **kwargs : Additional keywords passed to `pyarrow.feather.write_feather`. pyarrowrfeatherz'feather only support IO with DataFrameswbFstorage_optionsis_textN) rrr isinstancer ValueErrorr write_featherhandle)dfpathrkwargsrhandless W/var/www/html/land-tabula/venv/lib/python3.13/site-packages/pandas/io/feather_format.py to_featherr''sc4y) b) $ $BCC  dOU  b..;F;   s A%% A3pandasTc  [S5 SSKJn SSKn[ U5 [ USUSS9nU[ RLaw[5(dh[R"5 [R"SS [5 URURU[U5S 9sSSS5 sSSS5 $UR!URU[U5S 9n[#XS 9sSSS5 $!,(df  ND=f!,(df  g=f) a> Load a feather-format object from the file path. Feather is particularly useful for scenarios that require efficient serialization and deserialization of tabular data. It supports schema preservation, making it a reliable choice for use cases such as sharing data between Python and R, or persisting intermediate results during data processing pipelines. This method provides additional flexibility with options for selective column reading, thread parallelism, and choosing the backend for data types. Parameters ---------- path : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``read()`` function. The string could be a URL. Valid URL schemes include http, ftp, s3, gs and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.feather``. columns : sequence, default None If not provided, all columns are read. use_threads : bool, default True Whether to parallelize reading using multiple threads. storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. dtype_backend : {{'numpy_nullable', 'pyarrow'}} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`. * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` :class:`DataFrame` .. versionadded:: 2.0 Returns ------- type of object stored in file DataFrame object stored in the file. See Also -------- read_csv : Read a comma-separated values (csv) file into a pandas DataFrame. read_excel : Read an Excel file into a pandas DataFrame. read_spss : Read an SPSS file into a pandas DataFrame. read_orc : Load an ORC object into a pandas DataFrame. read_sas : Read SAS file into a pandas DataFrame. Examples -------- >>> df = pd.read_feather("path/to/file.feather") # doctest: +SKIP rrrNrbFrignorezmake_block is deprecated)columns use_threads) dtype_backend)rrr(pandas.core.arrays.arrow.extension_typesr rr no_defaultrwarningscatch_warningsfilterwarningsr read_featherr!bool read_tabler ) r#r,r-rr.rr(r%pa_tables r&r4r4MsJy)4 &  dOU   CNN *3E3G3G((*''." ++NNGkAR,+*   %% NNGk9J& %XK%  +*   s*8C?%AC.% C?8,C?. C< 8C?? D )N) r"r r#zFilePath | WriteBuffer[bytes]rStorageOptions | Noner$rreturnNone) r#zFilePath | ReadBuffer[bytes]r,zSequence[Hashable] | Noner-r5rr8r.zDtypeBackend | lib.NoDefaultr9r )%__doc__ __future__rtypingrrr1pandas._configr pandas._libsrpandas.compat._optionalr pandas.errorsr pandas.util._decoratorsr pandas.util._validatorsr pandas.core.apir pandas.io._utilr pandas.io.commonrcollections.abcrrpandas._typingrrrrrr'r0r4r&rKs"->(.7%1' .2#<#< '#<+#< #<  #