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K((**CF!*o112I2IJJC Oz 3D11DHH#)/;77 5/(((..CI2244 E22DH   rcrc |j|jdS)N)rr)rrGrs rarzJSONTableWriter.obj_to_writes+tx888rcr)rHrIrJrKrLrMrrOrPrKrUrOrQrRrWrMr[r\r)rrrrrrr __classcell__)rs@rarrbsyOEIJJJJJJJX999X99999rcr)rHtypr convert_axes convert_dateskeep_default_dates precise_floatrPencodingencoding_errorsrSrTnrowsrX dtype_backendengine.FilePath | ReadBuffer[str] | ReadBuffer[bytes]rLiteral['frame']rDtypeArg | Nonerrbool | list[str]rrrr chunksizer int | NonerDtypeBackend | lib.NoDefaultrr;JsonReader[Literal['frame']]cdSr^r_rErHrrrrrrrPrrrSrrTrrXrrs ra read_jsonrs *$'3rc)rHrrrrrrPrrrSrTrrXrrLiteral['series']JsonReader[Literal['series']]cdSr^r_rs rarrs *%(Crc)rHrrrrrrPrrrSrrTrrXrrr"cdSr^r_rs rarrs *Src)rHrrrrrrrPrrrSrrTrrXrrrcdSr^r_rs rarrs *rcpandasrBstrictujsonLiteral['frame', 'series']DataFrame | Series | JsonReadercP|dkr|rtd|dkr|rtdt|||dkrd}||dkrd}t|fid|d|d|d |d |d |d |d |d| d| d| d| d|d|d| d|d|}| r|S|S)a * Convert a JSON string to pandas object. This method reads JSON files or JSON-like data and converts them into pandas objects. It supports a variety of input formats, including line-delimited JSON, compressed files, and various data representations (table, records, index-based, etc.). When `chunksize` is specified, an iterator is returned instead of loading the entire data into memory. Parameters ---------- path_or_buf : a str path, path object or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.json``. If you want to pass in a path object, pandas accepts any ``os.PathLike``. By file-like object, we refer to objects with a ``read()`` method, such as a file handle (e.g. via builtin ``open`` function) or ``StringIO``. orient : str, optional Indication of expected JSON string format. Compatible JSON strings can be produced by ``to_json()`` with a corresponding orient value. The set of possible orients is: - ``'split'`` : dict like ``{{index -> [index], columns -> [columns], data -> [values]}}`` - ``'records'`` : list like ``[{{column -> value}}, ... , {{column -> value}}]`` - ``'index'`` : dict like ``{{index -> {{column -> value}}}}`` - ``'columns'`` : dict like ``{{column -> {{index -> value}}}}`` - ``'values'`` : just the values array - ``'table'`` : dict like ``{{'schema': {{schema}}, 'data': {{data}}}}`` The allowed and default values depend on the value of the `typ` parameter. * when ``typ == 'series'``, - allowed orients are ``{{'split','records','index'}}`` - default is ``'index'`` - The Series index must be unique for orient ``'index'``. * when ``typ == 'frame'``, - allowed orients are ``{{'split','records','index', 'columns','values', 'table'}}`` - default is ``'columns'`` - The DataFrame index must be unique for orients ``'index'`` and ``'columns'``. - The DataFrame columns must be unique for orients ``'index'``, ``'columns'``, and ``'records'``. typ : {{'frame', 'series'}}, default 'frame' The type of object to recover. dtype : bool or dict, default None If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don't infer dtypes at all, applies only to the data. For all ``orient`` values except ``'table'``, default is True. convert_axes : bool, default None Try to convert the axes to the proper dtypes. For all ``orient`` values except ``'table'``, default is True. convert_dates : bool or list of str, default True If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates). keep_default_dates : bool, default True If parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if * it ends with ``'_at'``, * it ends with ``'_time'``, * it begins with ``'timestamp'``, * it is ``'modified'``, or * it is ``'date'``. precise_float : bool, default False Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality. date_unit : str, default None The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively. encoding : str, default is 'utf-8' The encoding to use to decode py3 bytes. encoding_errors : str, optional, default "strict" How encoding errors are treated. `List of possible values `_ . lines : bool, default False Read the file as a json object per line. chunksize : int, optional Return JsonReader object for iteration. See the `line-delimited json docs `_ for more information on ``chunksize``. This can only be passed if `lines=True`. If this is None, the file will be read into memory all at once. compression : str or dict, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and 'path_or_buf' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to ``None`` for no decompression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``. nrows : int, optional The number of lines from the line-delimited jsonfile that has to be read. This can only be passed if `lines=True`. If this is None, all the rows will be returned. 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 engine : {{"ujson", "pyarrow"}}, default "ujson" Parser engine to use. The ``"pyarrow"`` engine is only available when ``lines=True``. .. versionadded:: 2.0 Returns ------- Series, DataFrame, or pandas.api.typing.JsonReader A JsonReader is returned when ``chunksize`` is not ``0`` or ``None``. Otherwise, the type returned depends on the value of ``typ``. See Also -------- DataFrame.to_json : Convert a DataFrame to a JSON string. Series.to_json : Convert a Series to a JSON string. json_normalize : Normalize semi-structured JSON data into a flat table. Notes ----- Specific to ``orient='table'``, if a :class:`DataFrame` with a literal :class:`Index` name of `index` gets written with :func:`to_json`, the subsequent read operation will incorrectly set the :class:`Index` name to ``None``. This is because `index` is also used by :func:`DataFrame.to_json` to denote a missing :class:`Index` name, and the subsequent :func:`read_json` operation cannot distinguish between the two. The same limitation is encountered with a :class:`MultiIndex` and any names beginning with ``'level_'``. Examples -------- >>> from io import StringIO >>> df = pd.DataFrame( ... [["a", "b"], ["c", "d"]], ... index=["row 1", "row 2"], ... columns=["col 1", "col 2"], ... ) Encoding/decoding a Dataframe using ``'split'`` formatted JSON: >>> df.to_json(orient="split") '{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}' >>> pd.read_json(StringIO(_), orient="split") # noqa: F821 col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'index'`` formatted JSON: >>> df.to_json(orient="index") '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}' >>> pd.read_json(StringIO(_), orient="index") # noqa: F821 col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. >>> df.to_json(orient="records") '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]' >>> pd.read_json(StringIO(_), orient="records") # noqa: F821 col 1 col 2 0 a b 1 c d Encoding with Table Schema >>> df.to_json(orient="table") '{"schema":{"fields":[{"name":"index","type":"string","extDtype":"str"},{"name":"col 1","type":"string","extDtype":"str"},{"name":"col 2","type":"string","extDtype":"str"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}' The following example uses ``dtype_backend="numpy_nullable"`` >>> data = '''{"index": {"0": 0, "1": 1}, ... "a": {"0": 1, "1": null}, ... "b": {"0": 2.5, "1": 4.5}, ... "c": {"0": true, "1": false}, ... "d": {"0": "a", "1": "b"}, ... "e": {"0": 1577.2, "1": 1577.1}}''' >>> pd.read_json(StringIO(data), dtype_backend="numpy_nullable") index a b c d e 0 0 1 2.5 True a 1577.2 1 1 4.5 False b 1577.1 rrz)cannot pass both dtype and orient='table'z0cannot pass both convert_axes and orient='table'NTrHrrrrrrrPrrSrrTrrXrrr)rr JsonReaderread)rErHrrrrrrrPrrrSrrTrrXrr json_readers rarrslUDEEE \KLLL &&& }7** ' 1 1 v Ce  "\  $m .-$m)e) Ke( (!"$m#$v%K*"!!!rczpandas.api.typingceZdZdZddejdfd>dZdZd?dZe d@d#Z e dAd&Z e dBd)Z dCd*Z dDd+Z dCd,Z dEd.Z dFd/ZdGd1Ze d@d2Ze dAd3Ze dBd4ZdCd5ZdGd6ZdHd=ZdS)Irz JsonReader provides an interface for reading in a JSON file. If initialized with ``lines=True`` and ``chunksize``, can be iterated over ``chunksize`` lines at a time. Otherwise, calling ``read`` reads in the whole document. NrrrrArrVrrOrrSrrrTr6rrXrkrrIrrrr;r[r\c||_||_||_||_||_||_||_| |_| |_||_ | |_ ||_ | |_ | |_ d|_||_||_d|_||_|j dvrt'd|j d|j Kt)d|j d|_ |j st'd|j dkrt'd |j1t)d |jd|_|j st'd |j dkr|j st'd ||_dS|j d krb||}|j s=|js6|5||_ddddS#1swxYwYdS||_dSdS)Nr>rpyarrowzThe engine type z is currently not supported.rrz*chunksize can only be passed if lines=Truerz %-k4>1MMDN: O !MNNN{i'' R : !)'4:qAADJ: K !IJJJ ;) # #:  5+DIII [G # #//0BCCDN !dj !,, $ DI,,,,,,,,,,,,,,,,,,! $ #s F22F69F6ct|} t|d|j|j|j|j|_n&#t$r}td|d|d}~wwxYw|jj }|S)z The function read_json accepts three input types: 1. filepath (string-like) 2. file-like object (e.g. open file object, StringIO) r)rrTrXerrorszFile z does not existN) r,r*rrTrXrrOSErrorFileNotFoundErrorr)rrerrs rarz"JsonReader._get_data_from_filepaths ,,>?? %" , $ 4+ DLL   #;*;;;  "\0!!s.A A# AA#rKc\dddd|DDdS)zG Combines a list of JSON objects into one JSON object. 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If an open stream or file was passed, we leave it open. N)rclosers rarAzJsonReader.close-s. < # L    $ #rcr c|Sr^r_rs ra__iter__zJsonReader.__iter__7 rccdSr^r_rs ra__next__zJsonReader.__next__:sCF3rccdSr^r_rs rarFzJsonReader.__next__=sADrccdSr^r_rs rarFzJsonReader.__next__@s !Srcc|jr+|j|jkr|tt t |j|j}|s|t ||}| |}t|j|jt|z|_ |xjt|z c_n(#t$r}||d}~wwxYw|jtjurFt#dd5|d|jcdddS#1swxYwYdS|S)Nr1Fr2)rrrA StopIterationr4rrrrr5rangerrU Exceptionrrr6rr7)rrSr8rGexs rarFzJsonReader.__next__Es : $/TZ77 JJLLL VDIt~6677 JJLLL  ,,U33J))*55CdotS/IJJCI OOs3xx 'OOO    JJLLLH   S^ 3 3 ?GG  ))"'t7I*                   Js+8A6C// D9DD:E##E'*E'c|Sr^r_rs ra __enter__zJsonReader.__enter__brDrcexc_typetype[BaseException] | None exc_valueBaseException | None tracebackTracebackType | Nonec.|dSr^)rA)rrPrRrTs ra__exit__zJsonReader.__exit__es rc)rrArrVrrOrrOrSrOrrrTr6rrrXrkrrIrrrr;r[r\r)rrr[r)rrr[r")rrr[rr[rr[r)r:rKr[rr)r[r )rPrQrRrSrTrUr[r\)rrr__doc__rr6rrrrrrrr5rArCrFrOrWr_rcrarrXs026&.69n$'H!H!H!H!H!T""".    BBBXB @@@X@ UUUXU    24EEEE2!!!!FFFXF DDDXD !!!X!:rcrceZdZUded<ded<dZdddd dZded <d d d d d d ejfd-dZe d.dZ e d/dZ d/d Z e d0d"Z d#Ze d1d2d+Ze d3d,Zd S)4Parserztuple[str, ...] _split_keysrKrrti3l,b/l`'rlF[L'r:NTFrrrrOrrrrrrr[r\c L||_||j}||_||_|G|}||jvrt d|j|j||_n|jd|_||_ ||_ ||_ ||_ ||_ | |_dS)Nzdate_unit must be one of ru)r:rrHrlower _STAMP_UNITSr _MIN_STAMPS min_stamprrrrPrr) rr:rHrrrrrrPrs rarzParser.__init__{s >)F   !))I 111 !PT=N!P!PQQQ!-i8DNN!-c2DN*(*""4*rcdecodedr"ct|t|j}|r'd|}t d|dS)zT Checks that dict has only the appropriate keys for orient='split'. z, z!JSON data had unexpected key(s): N)rkeys differencer]rr)rrcbad_keysbad_keys_joineds racheck_keys_splitzParser.check_keys_splitso w||~~&&11#d6F2G2GHH  T"ii11ORRRSS S T Trcrc|}|jr||}||}|Sr^)_parser _convert_axes_try_convert_typesrs rar=z Parser.parsesEkkmm   *$$S))C%%c** rcc t|r^rrs rarkz Parser._parserrcrGc|jD]u}||}t||jd}|||ddd\}}|r(t ||jd}t |||v|S)z& Try to convert axes. F)rrT)rsr use_dtypesris_axis) _AXIS_ORDERS _get_axisr"r_try_convert_datar setattr)rrG axis_nameaxsernew_serresultnew_axiss rarlzParser._convert_axess ) 2 2Iy))B28%888C"44 " 5OGV 2  EJJJY111 rcc t|r^rrs rarmzParser._try_convert_typesrrcrsr3rr"rprqtuple[Series, bool]c|}|r|jsCtt|r|dfS|tj}|dfS|jdurnt ||j|spt|jtr|j |n|j}|3 | |dfS#ttf$r|dfcYSwxYw|r||} | |ur| dfSd} |jt jur|s|dfSt%|jr0 | d}d} n#ttf$rYnwxYw|jjdkr;|jdkr0 | d}d} n#ttf$rYnwxYwt)|rY|jdvrP | d} | |kr| }d} n#ttt*f$rYnwxYw|jdkr;|jdkr0 | d}d} n#ttf$rYnwxYw|d krt)|r|jd kr|dfS|| fS) zI Try to parse a Series into a column by inferring dtype. FTNfloat64f)floatobjectint64rMrUr)rallr$fillnarnan_should_convert_datesrrr"getastype TypeErrorr_try_convert_to_daterrr6rr{r OverflowErrorrH) rrsrrprrqorg_datafilledrnew_data converteds rartzParser._try_convert_datas"  +: +uT{{##';&RV,,t|#t##*t6 + -7tz4,H,HXDJNN4(((dj$+#{{511477%z2+++#U{***+  &0066Ht##~%  S^ 3 3G 3:  TZ ( (  {{9-- z*     :?c ! !djI&=&= {{9-- z*     t99 '::: #??733$))++%#D $Iz=9     :  4:#8#8 {{7++ z*     7??s4yy?{g%%U{"YsZ-CCC.EEE9FF%$F%1G33H  H 'H??IIct|s|S|}|jdks |jdkr; |d}n$#t$r|cYStt f$rYnwxYwt |jjtj rGt|j ||j kz|j tkz}|s|S|jdkr|tj5tjdt$dD]2} t'|d|ccdddS#t($rY/wxYw dddn #1swxYwYnq|jr|jfn|j}|D]X} t'|d| }|jd }n(#t2$rY=t ttf$rYUwxYw|S) z Try to parse an ndarray like into a date column. 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