o Gh]@sdZddlmZddlZddlZddlZddlmZmZm Z ddl Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZddlZdd lmZmZddlmZddl m!Z!ddl"m#Z#m$Z$m%Z%m&Z&m'Z'erddl(m)Z)m*Z*m+Z+m,Z,m-Z-dEddZ.   dFdGd$d%Z/Gd&ddZ0Gd'd(d(e0Z1Gd)d*d*e0Z2eedd+  , -    dHdId9d:Z3eedd+d,ddej4ej4ddfdJdCdDZ5dS)Kz parquet compat ) annotationsN) TYPE_CHECKINGAnyLiteral)catch_warnings)using_pyarrow_string_dtype)lib)import_optional_dependencyAbstractMethodError)doc)find_stack_level)check_dtype_backend) DataFrame get_option) _shared_docs)arrow_string_types_mapper) IOHandles get_handle is_fsspec_urlis_urlstringify_path) DtypeBackendFilePath ReadBufferStorageOptions WriteBufferenginestrreturnBaseImplc Cs|dkrtd}|dkr>ttg}d}|D]"}z|WSty6}z|dt|7}WYd}~qd}~wwtd||dkrEtS|dkrLtStd ) zreturn our implementationautozio.parquet.enginez - NzUnable to find a usable engine; tried using: 'pyarrow', 'fastparquet'. A suitable version of pyarrow or fastparquet is required for parquet support. Trying to import the above resulted in these errors:pyarrow fastparquetz.engine must be one of 'pyarrow', 'fastparquet')r PyArrowImplFastParquetImpl ImportErrorr ValueError)rengine_classes error_msgs engine_classerrr-^/var/www/html/HyperOracle/crypto_trader/venv/lib/python3.10/site-packages/pandas/io/parquet.py get_engine2s,  r/rbFpath1FilePath | ReadBuffer[bytes] | WriteBuffer[bytes]fsrstorage_optionsStorageOptions | Nonemodeis_dirboolVtuple[FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any]c Cs\t|}|dur;tddd}tddd}|dur%t||jr%|r$tdn|dur1t||jjr1n tdt|j t |r}|dur}|durftd}td}z |j |\}}Wn t |j fyeYnw|dur|td}|jj|fi|pwi\}}n|rt|r|d krtd d} |s|st|trtj|st||d |d } d}| j}|| |fS) zFile handling for PyArrow.Nz pyarrow.fsignore)errorsfsspecz8storage_options not supported with a pyarrow FileSystem.z9filesystem must be a pyarrow or fsspec FileSystem, not a r#r0z8storage_options passed with buffer, or non-supported URLFis_textr4)rr isinstance FileSystemNotImplementedErrorspecAbstractFileSystemr(type__name__rfrom_uri TypeError ArrowInvalidcore url_to_fsrrosr1isdirrhandle) r1r3r4r6r7path_or_handlepa_fsr<pahandlesr-r-r._get_path_or_handleTsf       rRc@s0eZdZed ddZd ddZddd d Zd S)r dfrrNonecCst|ts tddS)Nz+to_parquet only supports IO with DataFrames)r?rr()rSr-r-r.validate_dataframes zBaseImpl.validate_dataframecKt|Nr )selfrSr1 compressionkwargsr-r-r.writezBaseImpl.writeNcKrVrWr )rXr1columnsrZr-r-r.readr\z BaseImpl.read)rSrrrT)rSrrW)rr)rE __module__ __qualname__ staticmethodrUr[r^r-r-r-r.r s   c@sFeZdZdddZ     ddddZdddejddfdddZdS) r%rrTcCs&tdddddl}ddl}||_dS)Nr#z(pyarrow is required for parquet support.extrar)r pyarrow.parquet(pandas.core.arrays.arrow.extension_typesapi)rXr#pandasr-r-r.__init__s  zPyArrowImpl.__init__snappyNrSrr1FilePath | WriteBuffer[bytes]rY str | Noneindex bool | Noner4r5partition_colslist[str] | NonecKsF||d|ddi} |dur|| d<|jjj|fi| } |jr:dt|ji} | jj } i| | } | | } t |||d|dud\}}}t |t jrgt|drgt |jttfrg|j}t |trg|}z1|dur}|jjj| |f|||d|n|jjj| |f||d|W|dur|dSdS|dur|ww) Nschemapreserve_index PANDAS_ATTRSwb)r4r6r7name)rYrn filesystem)rYru)rUpoprfTable from_pandasattrsjsondumpsrpmetadatareplace_schema_metadatarRr?ioBufferedWriterhasattrrtrbytesdecodeparquetwrite_to_dataset write_tableclose)rXrSr1rYrlr4rnrurZfrom_pandas_kwargstable df_metadataexisting_metadatamerged_metadatarNrQr-r-r.r[sj        zPyArrowImpl.writeFuse_nullable_dtypesr8 dtype_backendDtypeBackend | lib.NoDefaultcKs"d|d<i} |dkrddlm} | } | j| d<n|dkr#tj| d<ntr+t| d<td} | d kr7d| d <t|||d d \} }}zD|j j j | f|||d |}|j di| }| d kre|j d dd}|jjr{d|jjvr{|jjd}t||_|W|dur|SS|dur|ww)NTuse_pandas_metadatanumpy_nullabler)_arrow_dtype_mapping types_mapperr#zmode.data_managerarray split_blocksr0)r4r6)r]rufiltersF)copys PANDAS_ATTRSr-)pandas.io._utilrgetpd ArrowDtyperrrrRrfr read_table to_pandas _as_managerrpr|rzloadsryr)rXr1r]rrrr4rurZto_pandas_kwargsrmappingmanagerrNrQpa_tableresultrr-r-r.r^sT           zPyArrowImpl.readrrTriNNNN)rSrr1rjrYrkrlrmr4r5rnrorrT)rr8rrr4r5rr)rEr_r`rhr[r no_defaultr^r-r-r-r.r%s  Dr%c@s@eZdZdddZ     ddd dZ    ddddZdS)r&rrTcCstddd}||_dS)Nr$z,fastparquet is required for parquet support.rb)r rf)rXr$r-r-r.rh's zFastParquetImpl.__init__riNrSrrY*Literal['snappy', 'gzip', 'brotli'] | Noner4r5c  s||d|vr|durtdd|vr|d}|dur"d|d<|dur*tdt|}t|r@tdfdd|d <nrFtd td d |jj ||f|||d |WddS1sfwYdS)N partition_onzYCannot use both partition_on and partition_cols. Use partition_cols for partitioning datahive file_scheme9filesystem is not implemented for the fastparquet engine.r<csj|dfip iS)Nrs)open)r1_r<r4r-r.Rs z'FastParquetImpl.write.. open_withz?storage_options passed with file object or non-fsspec file pathT)record)rY write_indexr) rUr(rvrArrr rrfr[) rXrSr1rYrlrnr4rurZr-rr.r[/sB   "zFastParquetImpl.writec Ksi}|dd}|dtj} d|d<|rtd| tjur"td|dur*tdt|}d} t|rHtd} | j|d fi|pAij |d <nt |t r^t j |s^t|d d|d } | j}z|jj|fi|} | jd ||d |W| dur}| SS| dur| ww)NrFr pandas_nullszNThe 'use_nullable_dtypes' argument is not supported for the fastparquet enginezHThe 'dtype_backend' argument is not supported for the fastparquet enginerr<r0r3r=)r]rr-)rvrrr(rArrr rr3r?rrKr1rLrrMrf ParquetFilerr) rXr1r]rr4rurZparquet_kwargsrrrQr< parquet_filer-r-r.r^dsD     zFastParquetImpl.readrr)rSrrYrr4r5rrT)NNNN)r4r5rr)rEr_r`rhr[r^r-r-r-r.r&&s   8r&)r4r!rirSr$FilePath | WriteBuffer[bytes] | NonerYrkrlrmrnroru bytes | Nonec Kspt|tr|g}t|} |durtn|} | j|| f|||||d||dur6t| tjs2J| SdS)a Write a DataFrame to the parquet format. Parameters ---------- df : DataFrame path : str, path object, file-like object, or None, default None String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. If None, the result is returned as bytes. If a string, it will be used as Root Directory path when writing a partitioned dataset. The engine fastparquet does not accept file-like objects. .. versionchanged:: 1.2.0 engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used. The default ``io.parquet.engine`` behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. When using the ``'pyarrow'`` engine and no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first. Use the filesystem keyword with an instantiated fsspec filesystem if you wish to use its implementation. compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}}, default 'snappy'. Name of the compression to use. Use ``None`` for no compression. index : bool, default None If ``True``, include the dataframe's index(es) in the file output. If ``False``, they will not be written to the file. If ``None``, similar to ``True`` the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output. partition_cols : str or list, optional, default None Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string. {storage_options} .. versionadded:: 1.2.0 filesystem : fsspec or pyarrow filesystem, default None Filesystem object to use when reading the parquet file. Only implemented for ``engine="pyarrow"``. .. versionadded:: 2.1.0 kwargs Additional keyword arguments passed to the engine Returns ------- bytes if no path argument is provided else None N)rYrlrnr4ru)r?rr/r~BytesIOr[getvalue) rSr1rrYrlr4rnrurZimpl path_or_bufr-r-r. to_parquets( F rFilePath | ReadBuffer[bytes]r]rbool | lib.NoDefaultrrr&list[tuple] | list[list[tuple]] | Nonec Ksft|} |tjurd} |dur| d7} tj| ttdnd}t|| j|f||||||d|S)a Load a parquet object from the file path, returning a DataFrame. 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.parquet``. A file URL can also be a path to a directory that contains multiple partitioned parquet files. Both pyarrow and fastparquet support paths to directories as well as file URLs. A directory path could be: ``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``. engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used. The default ``io.parquet.engine`` behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable. When using the ``'pyarrow'`` engine and no storage options are provided and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec`` (e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first. Use the filesystem keyword with an instantiated fsspec filesystem if you wish to use its implementation. columns : list, default=None If not None, only these columns will be read from the file. {storage_options} .. versionadded:: 1.3.0 use_nullable_dtypes : bool, default False If True, use dtypes that use ``pd.NA`` as missing value indicator for the resulting DataFrame. (only applicable for the ``pyarrow`` engine) As new dtypes are added that support ``pd.NA`` in the future, the output with this option will change to use those dtypes. Note: this is an experimental option, and behaviour (e.g. additional support dtypes) may change without notice. .. deprecated:: 2.0 dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable' Back-end data type applied to the resultant :class:`DataFrame` (still experimental). Behaviour is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` (default). * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` DataFrame. .. versionadded:: 2.0 filesystem : fsspec or pyarrow filesystem, default None Filesystem object to use when reading the parquet file. Only implemented for ``engine="pyarrow"``. .. versionadded:: 2.1.0 filters : List[Tuple] or List[List[Tuple]], default None To filter out data. Filter syntax: [[(column, op, val), ...],...] where op is [==, =, >, >=, <, <=, !=, in, not in] The innermost tuples are transposed into a set of filters applied through an `AND` operation. The outer list combines these sets of filters through an `OR` operation. A single list of tuples can also be used, meaning that no `OR` operation between set of filters is to be conducted. Using this argument will NOT result in row-wise filtering of the final partitions unless ``engine="pyarrow"`` is also specified. For other engines, filtering is only performed at the partition level, that is, to prevent the loading of some row-groups and/or files. .. versionadded:: 2.1.0 **kwargs Any additional kwargs are passed to the engine. Returns ------- DataFrame See Also -------- DataFrame.to_parquet : Create a parquet object that serializes a DataFrame. Examples -------- >>> original_df = pd.DataFrame( ... {{"foo": range(5), "bar": range(5, 10)}} ... ) >>> original_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> df_parquet_bytes = original_df.to_parquet() >>> from io import BytesIO >>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes)) >>> restored_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> restored_df.equals(original_df) True >>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"]) >>> restored_bar bar 0 5 1 6 2 7 3 8 4 9 >>> restored_bar.equals(original_df[['bar']]) True The function uses `kwargs` that are passed directly to the engine. In the following example, we use the `filters` argument of the pyarrow engine to filter the rows of the DataFrame. Since `pyarrow` is the default engine, we can omit the `engine` argument. Note that the `filters` argument is implemented by the `pyarrow` engine, which can benefit from multithreading and also potentially be more economical in terms of memory. >>> sel = [("foo", ">", 2)] >>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel) >>> restored_part foo bar 0 3 8 1 4 9 zYThe argument 'use_nullable_dtypes' is deprecated and will be removed in a future version.TzFUse dtype_backend='numpy_nullable' instead of use_nullable_dtype=True.) stacklevelF)r]rr4rrru) r/rrwarningswarn FutureWarningr rr^) r1rr]r4rrrurrZrmsgr-r-r. read_parquets0 r)rrrr )Nr0F) r1r2r3rr4r5r6rr7r8rr9)Nr!riNNNN)rSrr1rrrrYrkrlrmr4r5rnrorurrr)r1rrrr]ror4r5rrrrrurrrrr)6__doc__ __future__rr~rzrKtypingrrrrrpandas._configr pandas._libsrpandas.compat._optionalr pandas.errorsr pandas.util._decoratorsr pandas.util._exceptionsr pandas.util._validatorsrrgrrrpandas.core.shared_docsrrrpandas.io.commonrrrrrpandas._typingrrrrrr/rRr r%r&rrrr-r-r-r.sb            % ?  q ]