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õFðP ò9ó ô9r] rm )rO Útyprº Ú convert_axesÚ convert_datesÚkeep_default_datesÚ precise_floatrS ÚencodingÚencoding_errorsrU rV ÚnrowsrI Ú dtype_backendÚenginec ó y rK rL ©rH rO rÁ rº r rà rÄ rÅ rS rÆ rÇ rU Ú chunksizerV rÈ rI rÉ rÊ s rZ Ú read_jsonrÎ ó ð, r] )rO rº r rà rÄ rÅ rS rÆ rÇ rU rV rÈ rI rÉ rÊ c ó y rK rL rÌ s rZ rÎ rÎ ® rÏ r] )rO rº r rà rÄ rÅ rS rÆ rÇ rU rÍ rV rÈ rI rÉ rÊ c ó y rK rL rÌ s rZ rÎ rÎ Ç rÏ r] )rO rÁ rº r rà rÄ rÅ rS rÆ rÇ rU rÍ rV rÈ rI rÉ rÊ c ó y rK rL rÌ s rZ rÎ rÎ à rÏ r] Údecompression_options)rI rÓ rE ÚstrictÚujsonc ó& |dk( r |r t d« |dk( r |r t d« t |« ||dk7 rd}||dk7 rd}t | fi d|d|d|d|d |d
|
|d |d |d | d| d| d| d|d|d|
|
d|d|}| r|S |j « S )a|"
|
Convert a JSON string to pandas object.
|
|
Parameters
|
----------
|
path_or_buf : a valid JSON str, 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``.
|
|
.. deprecated:: 2.1.0
|
Passing json literal strings is deprecated.
|
|
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
|
<https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
|
|
.. versionadded:: 1.3.0
|
|
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
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#line-delimited-json>`_
|
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.
|
{decompression_options}
|
|
.. versionchanged:: 1.4.0 Zstandard support.
|
|
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}
|
|
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
|
|
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')
|
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')
|
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')
|
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"}},{{"name":"col 1","type":"string"}},{{"name":"col 2","type":"string"}}],"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 <NA> 4.5 False b 1577.1
|
rg z)cannot pass both dtype and orient='table'z0cannot pass both convert_axes and orient='table'TrO rÁ rº r rà rÄ rÅ rS rÆ rU rÍ rV rÈ rI rÇ rÉ rÊ )rj r Ú
|
JsonReaderÚread)rH rO rÁ rº r rà rÄ rÅ rS rÆ rÇ rU rÍ rV rÈ rI rÉ rÊ Ú json_readers rZ rÎ rÎ ù s- ðZ ÒUÜÐDÓEÐEØ Ò\ÜÐKÓLÐLä Ô&à } 7Ò*ð ØÐ ¨'Ò 1Ø ä Øòáðñ ðñ ð ñ
|
"ð ñ $ð ñ .ðñ $ðñ ðñ ðñ ðñ ðñ ðñ ð ñ (ð ñ (ð!ñ" $ð#ñ$ ð%Kñ* ØÐà× Ñ Ó!Ð!r] c ó0 e Zd ZdZddej
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|
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.
|
NrÔ rÕ c óÞ || _ || _ || _ || _ || _ || _ || _ | | _ |
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|| _ | | _ | | _ d| _ || _ || _ d | _ || _ | j dvrt' d| j d« | j Mt) d| j d« | _ | j s t' d« | j dk( r t' d « | j 3t) d
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| j d« | _ | j s t' d « t+ |t, « r4| j s(d |v r$t/ j0 d t2 t5 « ¬« | j dk( r | j s t' d« || _ y | j dk( r(| j9 |« }| j; |« | _ y y )Nr > rÕ ÚpyarrowzThe engine type z is currently not supported.rÍ r¨ z*chunksize can only be passed if lines=TruerÜ z<currently pyarrow engine doesn't support chunksize parameterrÈ z&nrows can only be passed if lines=Trueú
|
úPassing literal json to 'read_json' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.©Ú
|
stacklevelzEcurrently pyarrow engine only supports the line-delimited JSON formatrÕ ) rO rÁ rº r rà rÄ rÅ rS rÆ rÊ rV rI rU rÍ Ú
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nrows_seenrÈ rÇ rv rÉ rj r4 rk ry ÚwarningsÚwarnÚ FutureWarningr r Ú_get_data_from_filepathÚ_preprocess_data)r~ Úfilepath_or_bufferrO rÁ rº r rà rÄ rÅ rS rÆ rU rÍ rV rÈ rI rÇ rÉ rÊ r s rZ r zJsonReader.__init__; sÚ ð* ØØ
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Ø (ÔØ *ÔØ"4Ô Ø *ÔØ"Ø Ø Ø&ÔØ .Ô Ø
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Ø"ØØ
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Ø .Ô Ø.2 Ø *Ôà ;;Ð 2Ñ 2Ü Ø" 4§;¡; -Ð/KÐLóð ð >>Ð %Ü -¨k¸4¿>¹>È1Ó MDNØ::Ü Ð!MÓNÐNØ{{iÒ'Ü ØRóð ð ::Ð !Ü)¨'°4·:±:¸qÓADJØ::Ü Ð!IÓJÐJä Ð)¬3Ô /ØJJØÐ*Ñ*ä MMðBô Ü+Ó-õ ð ;;)Ò #Ø::Ü ð5óð ð +DIØ [[GÒ #Ø×/Ñ/Ð0BÓCDØ ×-Ñ-¨dÓ3DIð $r] c óú t |d« r3| j s'| j s| 5 |j « }ddd« t |d« s#| j s | j r t |« }|S # 1 sw Y :xY w)a&
|
At this point, the data either has a `read` attribute (e.g. a file
|
object or a StringIO) or is a string that is a JSON document.
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|
If self.chunksize, we prepare the data for the `__next__` method.
|
Otherwise, we read it into memory for the `read` method.
|
rØ N)ÚhasattrrÍ rÈ rØ r )r~ r s rZ ræ zJsonReader._preprocess_data sa ô 4Ô ¨$¯.ª.¸D¿JºJØñ #Øyy{÷ #ätVÔ$¨$¯.ª.¸D¿JºJÜD>Dà ÷ #ð #ús §A1Á1A:c ó2 t |« }t |t « r!t |« st |« s t |« rVt |d| j | j | j | j ¬« | _ | j j }|S t |t « rP|j « j dt d t D « « z « rt |« st# d| d« t% j& dt( t+ « ¬« |S ) aÝ
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The function read_json accepts three input types:
|
1. filepath (string-like)
|
2. file-like object (e.g. open file object, StringIO)
|
3. JSON string
|
|
This method turns (1) into (2) to simplify the rest of the processing.
|
It returns input types (2) and (3) unchanged.
|
|
It raises FileNotFoundError if the input is a string ending in
|
one of .json, .json.gz, .json.bz2, etc. but no such file exists.
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Úr)rÆ rV rI Úerrors)ú.jsonc 3 ó& K | ] }d | yw)rí NrL )Ú.0Úcs rZ ú <genexpr>z5JsonReader._get_data_from_filepath.<locals>.<genexpr>¼ s è ø Ò"Q°1 U¨1¨#¤;Ñ"Qùs zFile z does not existrÞ rß )r0 rk ry r/ r- r+ r, rÆ rV rI rÇ rv rr ÚlowerÚendswithÚtupler* ÚFileNotFoundErrorrâ rã rä r )r~ rç s rZ rå z"JsonReader._get_data_from_filepath s ô ,Ð,>Ó ?ÐäÐ-¬sÔ3ÜÐ(Ô)Ü Ð /Ô0ÜÐ-Ô.ä%Ø"ØØ Ø × ,Ñ ,Ø $× 4Ñ 4Ø×+Ñ+ô DLð "&§ ¡ ×!4Ñ!4Ð ð" "Ð!ô Ð)¬3Ô /Ø"×(Ñ(Ó*×3Ñ3ØUÑ"QÔ8PÔ"QÓ QÑQôô Ð 2Ô3ä# eÐ,>Ð+?¸Ð$OÓPÐ Pä MMðBô Ü+Ó-õ ð "Ð!r] c óf ddj d |D « D cg c] }|s| c}« dS c c}w )zG
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Combines a list of JSON objects into one JSON object.
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ú[ú,c 3 ó< K | ] }|j « y wrK )Ústrip)rï Úlines rZ rñ z,JsonReader._combine_lines.<locals>.<genexpr>Ð s è ø Ò+K¸T¨D¯J©J¯LÑ+Kùs ú])Újoin)r~ rU rû s rZ Ú_combine_lineszJsonReader._combine_linesË s6 ð
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Ñ+KÀUÔ+KÖT 4Êt$ÒTÓUÐVÐVWÐ Xð
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ùÚTs .
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.
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c ó y rK rL r s rZ rØ zJsonReader.readÓ ó à r] c ó y rK rL r s rZ rØ zJsonReader.read× r r] c ó y rK rL r s rZ rØ zJsonReader.readÛ r r] c óT | 5 | j dk( rFt d« }|j | j « }t || j
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¬« cddd« S | j dk( r+| j r·| j r t | « }nº| j rLt t | j | j « « }| j |« }| j |« }nbt | j « }|j d« }| j | j |« « }n| j | j « }| j
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t j" ur&|j% d| j
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¬« cddd« S |cddd« S ddd« y# 1 sw Y yxY w) zA
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Read the whole JSON input into a pandas object.
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rÜ z pyarrow.json)rÉ NrÕ rÝ F©Ú infer_objectsrÉ )rÊ r rÎ r r' rÉ rU rÍ r% rÈ Úlistr rþ Ú_get_object_parserr r r Ú
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no_defaultÚconvert_dtypes)r~ Ú pyarrow_jsonÚpa_tablerN rU Ú
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lines_jsonr Ú
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data_liness rZ rØ zJsonReader.readß s^ ð
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ñ Ø{{iÒ'Ü 9¸.Ó I Ø'×1Ñ1°$·)±)Ó<Ü,¨XÀT×EWÑEWÔX÷ ñ ð
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(÷ ÷ ñ ús A F ÁD&F Æ F Æ F'c ó | j }| j }| j | j | j | j | j
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| j | j | j d}d}|dk( rt |fi |¤j « }|dk( s|/t |t « s||d< t |fi |¤j « }|S )z>
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Parses a json document into a pandas object.
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)rO rº r rà rÄ rÅ rS rÉ NrE rF rº )rÁ rº rO r rà rÄ rÅ rS rÉ Ú FrameParserÚparserk r Ú SeriesParser)r~ ÚjsonrÁ rº ÚkwargsrN s rZ r z JsonReader._get_object_parserþ sÀ ð hhØ
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àkkØZZØ × -Ñ -Ø !× /Ñ /Ø"&×"9Ñ"9Ø !× /Ñ /Ø Ø !× /Ñ /ñ
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ð Ø '>Ü dÑ- fÑ-×3Ñ3Ó5Cà (?ckÜ e¤TÔ*Ø"'wÜ tÑ. vÑ.×4Ñ4Ó6Cà
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r] c óR | j | j j « yy)z©
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If we opened a stream earlier, in _get_data_from_filepath, we should
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close it.
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If an open stream or file was passed, we leave it open.
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N)rv Úcloser s rZ r zJsonReader.close s# ð <<Ð #Ø LL× Ñ Õ ð $r] c ó | S rK rL r s rZ Ú__iter__zJsonReader.__iter__# ó Ø r] c ó y rK rL r s rZ Ú__next__zJsonReader.__next__& r r] c ó y rK rL r s rZ r zJsonReader.__next__* r r] c ó y rK rL r s rZ r zJsonReader.__next__. r r] c ó | j r/| j | j k\ r| j « t t t | j | j « « }|s| j « t | j |« }| j |« }t | j | j t |« z « |_ | xj t |« z c_ | j t j ur |j# d| j ¬« S |S # t $ r}| j « |d }~ww xY w)NFr )rÈ rá r Ú StopIterationr r r rÍ rþ r Úrangerµ rW Ú ExceptionrÉ r r r )r~ rU r rN Úexs rZ r zJsonReader.__next__2 s Ø ::$//¨T¯Z©ZÒ7Ø JJLÜ Ð äVDII t§~¡~Ó6Ó7ÙØ JJLÜ Ð ð Ø ×,Ñ,¨UÓ3JØ×)Ñ)¨*Ó5Cô doo¨t¯©ÄÀSÃÑ/IÓJCIØ OOs 3xÑ 'Oð
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× Ñ ¤S§^¡^Ñ 3Ø×%Ñ%Ø #°4×3EÑ3Eð &ó ð ð Jøô ò Ø JJLØHûð ús Á>A1D* Ä* E
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Ä3EÅE
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c ó | S rK rL r s rZ Ú __enter__zJsonReader.__enter__N r r] c ó$ | j « y rK )r )r~ Úexc_typeÚ exc_valueÚ tracebacks rZ Ú__exit__zJsonReader.__exit__Q s ð
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r] )rÁ rD r ú bool | NonerÄ r rÅ r rU r rÍ ú
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int | NonerV r8 rÈ r* rI úStorageOptions | NonerÇ r rÉ ú DtypeBackend | lib.NoDefaultrÊ r= r r r )r~ ú JsonReader[Literal['frame']]r r )r~ ú JsonReader[Literal['series']]r r! )r~ z&JsonReader[Literal['frame', 'series']]r úDataFrame | Series)r r/ r )r r@ )r% ztype[BaseException] | Noner& zBaseException | Noner' zTracebackType | Noner r )r r r Ú__doc__r r r ræ rå rþ r rØ r r r r r# r( rL r] rZ r× r× 2 s ñð0 26Ø&.Ø69·n±nØ $ð'N4ð ð N4ð "ð N4ð !ðN4ð ðN4ð ðN4ð ðN4ð (ð N4ð ð N4ð /ð!N4ð" $ð#N4ð$ 4ð%N4ð& ð'N4ð(
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ó)N4ò`ò ."ó`
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ð ò ó ð ð ò ó ð ð ò ó ð ó ó>ó6!óð ò ó ð ð ò ó ð ð ò ó ð óó8ðà,ðð (ðð (ð ð
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ô r] r× c ó e Zd ZU ded< ded< dZdddd dZded
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< d d d d d d ej f ddZe dd« Z
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e d « Z ddZ e dd« Z ddZe d dd« Ze dd« Zy ) ÚParserztuple[str, ...]Ú _split_keysry rz )ru ÚmsÚusÚnsi3ál ,b/ l `'ºrl F[L'r NTFc
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ón || _ | | j }|| _ || _ |K|j « }|| j
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|| _ || _ || _ d | _ | | _ y )Nzdate_unit must be one of ru )r rz rO rº rò Ú _STAMP_UNITSrj Ú _MIN_STAMPSÚ min_stamprÅ r rà rS rÄ rN rÉ )
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r~ r rO rº r rà rÄ rÅ rS rÉ s
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rZ r zParser.__init__g s½ ð à >Ø×)Ñ)Fà à
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à Ð Ø!Ó)IØ × 1Ñ 1Ñ1Ü Ð#<¸T×=NÑ=NÐ<OÐ!PÓQÐQØ !× -Ñ -¨iÑ 8DNà !× -Ñ -¨cÑ 2DNà *ÔØ (ÔØ *ÔØ"Ø"4Ô Ø.2Ø *Õr] c ó¼ t |j « « j t | j « « }|r dj |« }t d| « y)zT
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Checks that dict has only the appropriate keys for orient='split'.
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z, z!JSON data had unexpected key(s): N)r³ ÚkeysÚ
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differencer3 rý rj )r~ ÚdecodedÚbad_keysÚbad_keys_joineds rZ Úcheck_keys_splitzParser.check_keys_split sT ô
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w||~Ó&×1Ñ1´#°d×6FÑ6FÓ2GÓHÙ Ø "ii¨Ó 1OÜ Ð @À Ð@QÐ RÓSÐ Sð r] c ó¬ | j « | j y | j r| j « | j « | j S rK )Ú_parserN rÂ Ú _convert_axesÚ_try_convert_typesr s rZ r z Parser.parse sB à à 88Ð ØØ × Ò Ø × Ñ Ô Ø × Ñ Ô!Øxxr] c ó t | « rK r r s rZ rC z Parser._parse¡ r r] c ó. | j }|J |j D ]v }|j |« }t ||j d¬« }| j ||ddd¬« \ }}|sHt ||j d¬« }t | j ||« x y)z&
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Try to convert axes.
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NF)rº r· T)rh r Ú
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use_dtypesrà Úis_axis)rN Ú _AXIS_ORDERSÚ _get_axisr! rº Ú_try_convert_datar Úsetattr)r~ rN Ú axis_nameÚaxÚserÚnew_serÚresultÚnew_axiss rZ rD zParser._convert_axes¤ s ð
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ddt ¬« |j t j « }ddd« |dfS | j du rnWt | j t « r| j j |« n | j }| |j |« dfS |r| j! |« \ }} | r|dfS d}
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fS # 1 sw Y dfS xY w# t t f$ r |dfcY S w xY w# t t f$ r Y w xY w# t t f$ r Y êw xY w# t t t. f$ r Y Âw xY w# t t f$ r Y ¥w xY w)zI
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Try to parse a Series into a column by inferring dtype.
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FÚignorez Downcasting object dtype arrays©ÚcategoryNTÚfloat64Úf)ÚfloatÚobjectÚint64r rW r )rº Úallr# râ Úcatch_warningsÚfilterwarningsrä ÚfillnaÚnpÚnanrk ÚdictÚgetÚastypeÚ TypeErrorrj Ú_try_convert_to_daterÉ r r r Úkindrµ Ú OverflowErrorrO ) r~ rh r rH rà rI Úfilledrº Únew_datarR Ú converteds rZ rL zParser._try_convert_data¼ s¥ ñ Ø::ÜuT{Ô#Ø ;Ð&ä ×,Ñ,Ó.ñ 1Ü ×+Ñ+Ø Ø9Ü!.õð
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"[[¬¯©Ó 0F÷ 1ð t|Ð#àtÑ#Øô -7°t·z±zÄ4Ô,HDJJNN 4Ô(ÈdÏjÉjð ð Ð$ð+Ø #{{¨5Ó 1°4Ð 7Ð7ñ Ø #× 8Ñ 8¸Ó >Ñ HfÙØ ~Ð%à Ø × Ñ ¤S§^¡^Ñ 3¹Gà:Ð Ü TZZÔ (ð Ø{{ 9Ó-Ø ð ::??cÒ ! d§j¡j°IÒ&=ð Ø{{ 9Ó-Ø ô
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t9Ð':Ñ:ð Ø ;; wÓ/Ø Ñ$×)Ñ)Ô+Ø#DØ $Ið ::Ò 4§:¡:°Ò#8ð Ø{{ 7Ó+Ø ð
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7?s 4yØ{{gÒ%ØU{Ð"àYÐ ÷Q1ð t|Ð#ûô &¤zÐ2ò +Ø # U{Ò*ð+ûô" zÐ*ò Úð ûô zÐ*ò Ùð ûô z¬=Ð9ò Ùð ûô zÐ*ò Ùð úse »<H)ÃH8 Ä?I Å;I' Æ((I< Ç/J È)H5È8IÉ IÉI$É#I$É'I9É8I9É<JÊJÊJ(Ê'J(c ó2 t |« s|dfS |}|j dk( r|j t « }|j dk( r |j d« }t |j j t j « rMt |j « || j kD z |j t k( z }|j « s|dfS | j r | j fn | j" }|D ]O } t% j& « 5 t% j( ddt* ¬« t- |d|¬ « }d
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d
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t f$ r Y w xY w# 1 sw Y 8xY w# t t t
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f$ r Y w xY w) zÌ
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Try to parse a ndarray like into a date column.
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|
Try to coerce object in epoch/iso formats and integer/float in epoch
|
formats. Return a boolean if parsing was successful.
|
FÚstringr\ r] rV z=.*parsing datetimes with mixed time zones will raise an errorrW Úraise)rì ÚunitNT)rµ rº rf r\ rj rg rj Ú
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issubclassÚtyperb Únumberr" Ú_valuesr: r r^ rS r8 râ r_ r` rä r$ )r~ r rl Úin_rangeÚ
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date_unitsrS s rZ rh zParser._try_convert_to_date s ô 4yØ;Ð àà >>XÒ %Ø ¤vÓ.Hà >>XÒ %ð Ø ;; wÓ/ô hnn×)Ñ)¬2¯9©9Ô 5äX×%Ñ%Ó&ØdnnÑ,ñ.à×#Ñ#¤tÑ+ñ-ð ð
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Ø#ò "Ið
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Ü ×,Ñ,Ó.ñ UÜ ×+Ñ+Ø ð4ä!.õ ô +¨8¸GÈ)Ô TH÷Uð T>Ò !ð "ð U{Ðøô; !ò #ØU{Ò"Ü zÐ*ò Úð ú÷ Uð Uûô ¤ ¬yÐ9ò Ùð úsB ÁE Ã=E?Ä+E3Ä<E?Å E0Å E0Å/E0Å3E< Å8E?Å?FÆF)r ry rº úDtypeArg | Noner r rà úbool | list[str]rÄ r rÅ r rÉ r, r r )r> rd r r r )TTF) rh r5 r r! rH r rà ry rI r r útuple[Series, bool])r r! r rz )r r r r r8 r9 r r r r rA r rC rD rE rL rh rL r] rZ r2 r2 Z sZ
Ø Ó ØÓà*Là ØØ Ø ñ Kð Ið "&Ø !Ø*.Ø#(Ø #ØØ69·n±nð#+àð#+ð ð #+ð
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òY ó ðY ðv ò0ó ñ0r] r2 c ó0 e Zd ZU dZdZded< ddZddZy) r rW )rh rW r z Series | NonerN c ó, t | j | j ¬« }| j dk( rN|j « D ci c] \ }}t |« | }}}| j |« t di |¤| _ y t |« | _ y c c}}w )N©rÅ r rL ) r r rÅ rO Úitemsry rA r! rN )r~ r ÚkÚvr> s rZ rC zSeriesParser._parseQ sv Ü499°D×4FÑ4FÔGà ;;'Ò !Ø-1¯Z©Z«\×:¡T Q¨s1vqyÐ:GÑ:Ø × !Ñ ! 'Ô *Ü Ñ( Ñ(DHä d|DHùó ;s ÁBc ó | j y | j d| j | j ¬« \ }}|r|| _ y y )Nr ©rà )rN rL rà )r~ rN rR s rZ rE z SeriesParser._try_convert_types[ sJ Ø 88Ð Ø Ø×,Ñ,Ø DHH¨D×,>Ñ,>ð -ó
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Nrd r} )rº r rº rW )rº rO rg rL )r rO r r rÅ rN r~ ry rA rk r rô r) r. Ú from_dictr3 )r~ r rO r r r> ÚcolÚ
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orig_namess rZ rC zFrameParser._parsej su ØyyØà YÒ Ü ÜD°×0BÑ0BÔCÈ4ôDHð wÒ ô (¨¸D×<NÑ<NÔ O× UÑ UÓ W÷áAqô A ðGñ ð × !Ñ ! 'Ô *ð # 9Ñ-öàô *¨#¬tÔ 4s¸#Ñ=ðJð ô "-ØÜ(¨°TÓ:ó"GIÑ ô !Ñ7 tÐ7¨wÑ7DHØ wÒ Ü ×*Ñ*ÜD°×0BÑ0BÔCØØ ôDHð
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wÒ Ü)¨$¸d×>PÑ>PÔQDHä ÜD°×0BÑ0BÔCÈ4ôDHùó/ùò
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s Á3E8Â$$E>Nc ó |d }| j }|J d}i }t |j « « D ]' \ }\ }} ||« r |||« \ } }
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Take a conversion function and possibly recreate the frame.
|
Nc ó y)NTrL )r s rZ r® z0FrameParser._process_converter.<locals>.<lambda> r r] FTr¦ )rN Ú enumerater~ r rW rd ) r~ rZ ÚfiltrN Ú needs_new_objÚnew_objÚir rð rl rR Ú new_frames rZ Ú_process_converterz FrameParser._process_converter s§ ð <Ù#DàhhØÐ à ØÜ$ S§Y¡Y£[Ó1ò KAxQÙCyÙ#$ S¨!£9Ñ &ÙØ AØ$(MØGAJð ñ ä! '°·±Ô;IØ #§ ¡ IÔ Ø DHð r] c ó j y j r j « j fd« y )Nc ó, j | |d¬« S )NFr )rL ©r rð r~ s rZ r® z0FrameParser._try_convert_types.<locals>.<lambda>³ s ø 4×1Ñ1°#°qÈÐ1ÓN r] )rN rà Ú_try_convert_datesr r s `rZ rE z FrameParser._try_convert_types¬ s6 ø Ø 88Ð Ø Ø × Ò Ø × #Ñ #Ô %à × Ñ Û Nõ
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r] c ó® j y j }t |t « rg }t |« d fd } j fd|¬« y )Nc óÐ | v ryj syt | t « sy| j « }|j d« s |dk( s|dk( s|dk( s|j d« ryy)zK
|
Return if this col is ok to try for a date parse.
|
TF)Ú_atÚ_timeÚmodifiedÚdateÚdatetimeÚ timestamp)rÄ rk ry rò ró Ú
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startswith)r Ú col_lowerrà r~ s rZ Úis_okz-FrameParser._try_convert_dates.<locals>.is_okÀ so ø ð mÑ#ØØ×*Ò*Ø Ü c¤3Ô'Ø à Ià×"Ñ"Ð#3Ô4Ø
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Ò*Ø Ò&Ø
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Ò*Ø ×'Ñ'¨ Ô4àØr] c ó& j |« S rK )rh r s rZ r® z0FrameParser._try_convert_dates.<locals>.<lambda>Ö s ø ¨t×/HÑ/HÈÓ/K r] )r )r r )rN rà rk r r³ r )r~ Úconvert_dates_list_boolr rà s ` @rZ r z FrameParser._try_convert_dates¶ sV ù Ø 88Ð Ø ð #'×"4Ñ"4Ð Ü Ð-¬tÔ 4Ø&(Ð #ÜÐ 3Ó4 ö ð, × Ñ Ó KÐRWÐ ÕXr] r rK )rZ z1Callable[[Hashable, Series], tuple[Series, bool]]r z!Callable[[Hashable], bool] | Noner r )
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