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}BO  / )rYrGNrrrrGrYrWrrDsO rYrcn^\rSrSrSrSSU4Sjjjr\SSj5rSrU=r $) ribr]c H>[T U]UUUUUUUUU S9 US:waSUS3n [U 5e[XRS9UlUR(a [ U5nURS:Xa*[UR[5(a [S5eURS:Xa-UR[URR5;d=[URR!URR55(a S n [U 5eUR#S /S 9Rn S n [U 5(a(UR%5nS n XR'S5X'UR(dUR)S S9UlOr[URR,[.5(a5U (dUR%S S9nURR15UlUR)S S9UlSUlSUlXplg)z Adds a `schema` attribute with the Table Schema, resets the index (can't do in caller, because the schema inference needs to know what the index is, forces orient to records, and forces date_format to 'iso'. )rPrTrz8Trying to write with `orient='table'` and `date_format='zH'`. Table Schema requires dates to be formatted with `date_format='iso'`)rSz6orient='table' is not supported for MultiIndex columnsz/Overlapping names between the index and columns timedelta)includeFTc"UR5$rF) isoformat)xs rWrp*JSONTableWriter.__init__..s AKKMrY)droprrr]N)superrrvr.rSschemar0ndimrwr`r!rrdsetnamesr} intersection select_dtypesr~r| reset_indexrJrr to_timestamprLrK)rrJrKrLrMrurOrSrPrTr timedeltasr __class__s rWrJSONTableWriter.__init__es$        +   % !!, .;;  S/ !(JJ? ::#C(C 88q=Z Z@@%H  XX]S-A!As KK $ $SYY__ 5H H DCS/ !&& }&=EE  z??((*CF!o112IJCOzzD1DH#))//;77(((.CII224 E2DH   rYc4URURS.$)N)rr)rrJrs rWrJSONTableWriter.obj_to_writes++txx88rY)rLrSrJrKrr)rKrrLrrMrrurrOrrSrrPrrTrrrr) rrrrrrrrr __classcell__)rs@rWrrbsOEIJJ J  J  JJJBJJ JJX99rYr)rKtypr convert_axes convert_dateskeep_default_dates precise_floatrOencodingencoding_errorsrQrRnrowsrU dtype_backendenginecgrFrGrIrKrrrrrrrOrrrQ chunksizerRrrUrrs rW read_jsonrs*$'rY)rKrrrrrrOrrrQrRrrUrrcgrFrGrs rWrrs*%(rY)rKrrrrrrOrrrQrrRrrUrrcgrFrGrs rWrrs*rY)rKrrrrrrrOrrrQrrRrrUrrcgrFrGrs rWrrs*rYpandasrBstrictujsoncPUS:XaU(a [S5eUS:XaU(a [S5e[U5 UcUS:waSnUcUS:waSn[U40SU_SU_SU_SU_S U_S U_S U_S U_S U _SU _SU _SU _SU_SU_SU _SU_SU_6nU (aU$UR5$)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 rcz)cannot pass both dtype and orient='table'z0cannot pass both convert_axes and orient='table'TrKrrrrrrrOrrQrrRrrUrrr)rvr JsonReaderread)rIrKrrrrrrrOrrrQrrRrrUrr json_readers rWrrs-lUDEE \KLL & }7* ' 1    "  $ .$ ( (!"$#$%K*!!rYzpandas.api.typingcn\rSrSrSrSS\R S4SSjjrSrSSjr \ SS j5r \ SS j5r \ SS j5r SS jr SS jr SSjr S SjrS!SjrS"Sjr\ SSj5r\ SSj5r\ SSj5rSSjrS"SjrS#SjrSrg)$riXz 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. NrrcX lX0lX@lXPlX`lXplXlXlXlUUl Xl Xl Xl Xl SUlXlUUlSUlUUlURS;a['SURS35eURbS[)SURS5Ul UR(d ['S5eURS:Xa ['S 5eURb8[)S URS5UlUR(d ['S 5eURS:Xa#UR(d ['S 5eXlgURS :Xa\UR-U5nUR(d2UR(d!U UR/5UlSSS5 gUUlgg!,(df  g=f)Nr>rpyarrowzThe engine type z is currently not supported.rrz*chunksize can only be passed if lines=Truerz> %-k4>>1MDN:: !MNN{{i' R :: !)'4::qADJ:: !IJJ ;;) #:: 5+I [[G #//0BCDNNdjj $ DIT! $ Ts >G&& G4c [U5n[USURURURUR S9UlUR RnU$![an[SUS35UeSnAff=f)z The function read_json accepts three input types: 1. filepath (string-like) 2. file-like object (e.g. open file object, StringIO) r)rrRrUerrorszFile z does not existN) r,r*rrRrUrrOSErrorFileNotFoundErrorr)rrerrs rWr"JsonReader._get_data_from_filepaths ,,>? %" ,, $ 4 4++ DL"\\00!!  #*+?;  s;A A?*A::A?cvSSRSU5Vs/sH o"(dM UPM sn5S3$s snf)z7 Combines a list of JSON objects into one JSON object. [,c3@# UHoR5v M g7frF)strip).0lines rW ,JsonReader._combine_lines..s+KUTJJLLUs])join)rrQr s rW_combine_linesJsonReader._combine_liness< +KU+KT+K4t$+KTUVVW X Ts 6 6 cgrFrGrs rWrJsonReader.reads?BrYcgrFrGrs rWrrs=@rYcgrFrGrs rWrrsRUrYcU URS:XaUR5nO URS:XaUR5nSSS5 W$!,(df  W$=f)z1 Read the whole JSON input into a pandas object. rrN)r _read_pyarrow _read_ujsonrrJs rWrrsV {{i'((*'&&(   T  s AA Ac[S5nSn[UR[5(a[S5n/nURR 5HCupV[ U5n[U[ 5(dM'URXWR45 ME URU5nURUSS9nURURUS9n [XRS9n U $)z% Read JSON using the pyarrow engine. z pyarrow.jsonNrr[)explicit_schemaunexpected_field_behavior) parse_options)r)rrwrdictitemsrrappend pyarrow_dtyper ParseOptionsrrr'r) r pyarrow_jsonoptionspafieldsfieldrpd_dtyperpa_tabledfs rWrJsonReader._read_pyarrows2.A  djj$ ' '+I6BF $ 0 0 2 '.h 33MM5*@*@"AB!3 YYv&F"// &'0G ))$))7)K "8;M;M N rYcUR(aUR(a [U5nOUR(aL[ [ UR UR55nURU5nURU5nOb[UR 5nURS5nURURU55nOURUR 5nUR[RLa0[SS5 URSURS9sSSS5 $U$!,(df  g=f)z# Read JSON using the ujson engine.  future.distinguish_nan_and_naF infer_objectsrN)rQrr&rlistrrr_get_object_parserrrrr no_defaultrconvert_dtypes)rrJrQ lines_jsonr data_liness rWrJsonReader._read_ujsons ::~~TlVDIItzz:;!007 --j9!$)),!ZZ- --d.A.A*.MN))$))4C   S^^ 3 ?G))"'t7I7I*HG J HGs D== E c URnURnURURURURUR UR URURS.nUS:Xa[U40UD6R5$US:Xa3[U[5(dX4S'[U40UD6R5$[SU<S35e)z. 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