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B �5�g� � @ sr d dl mZ d dlmZmZ d dlmZ d dlZd dlmZ d dl m Z d dl m Z m Z mZ d dlZd dlm mZ d dlmZ d d lmZmZmZmZmZm Z d d l m Z d d l m Z m!Z!m"Z" d d l#m$Z$m%Z% d d l&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z, d dl-m.Z. d dl/m0Z0 d dl1m2Z2 d dl3m4Z4 d dl5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z; d dl<m=Z= d dl>m?Z?m@Z@ d dlAmBZB ejCZCejDZDdZEdGd d d d d!d d"d!d#d!d d$d%� d&d'�ZFG d(d)� d)e�ZGG d*d+� d+eG�ZHG d,d-� d-eG�ZIG d.d/� d/eI�ZJe"e.jKd0 d1�e d2dd3�e!d4d5gd6d7�dHd:d!d!d!d d!d;d#d;d$d<� d=d>����ZLG d?d@� d@ejM�ZNG dAdB� dB�ZOG dCdD� dDeO�ZPG dEdF� dFeO�ZQdS )I� )� annotations)�ABC�abstractmethod)�abcN)�StringIO)�islice)�Any�Callable�Mapping)�iNaT)�CompressionOptions�DtypeArg�FrameOrSeriesUnion� IndexLabel�JSONSerializable�StorageOptions)�AbstractMethodError)�deprecate_kwarg� deprecate_nonkeyword_arguments�doc)� ensure_str�is_period_dtype)� DataFrame� MultiIndex�Series�isna�notna� to_datetime)�generic)�!create_series_with_explicit_dtype)�NDFrame)�concat)� IOHandles� file_exists� get_handle� is_fsspec_url�is_url�stringify_path)�convert_to_line_delimits)�build_table_schema�parse_table_schema)�validate_integerz0.20.0�epoch� T�msF�inferr z str | None�str�int�boolz(Callable[[Any], JSONSerializable] | Noner r ) �obj�orient� date_format�double_precision� force_ascii� date_unit�default_handler�lines� compression�index�indent�storage_optionsc C s� | s|dkrt d��|r(|dkr(t d��|dkrLt|t�rL|j|jpFdd�}|dkrdt|t�rdt} n(t|t�rtt} nt|t�r�t} nt d��| |||||||| | d � � � }|r�t |�}| d k r�t | d | | d ��}|j � |� W d Q R X n|S d S ) N)�split�tablez?'index=False' is only valid when 'orient' is 'split' or 'table'�recordsz3'lines' keyword only valid when 'orient' is recordsr@ �values)�namez''obj' should be a Series or a DataFrame)r4 r5 r6 � ensure_asciir8 r9 r< r= �w)r; r> )� ValueError� isinstancer Zto_framerC r �JSONTableWriter� SeriesWriter� FrameWriter�NotImplementedError�writer( r$ �handle)� path_or_bufr3 r4 r5 r6 r7 r8 r9 r: r; r< r= r> �writer�s�handles� rR �J/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/io/json/_json.py�to_jsonL s@ rT c @ sZ e Zd ZU ded< dddddddddd �d d �Zd d � Zdd� Zeedd�dd���Z dS )�Writerr0 �_default_orientNr z str | Noner1 r2 z(Callable[[Any], JSONSerializable] | None)r4 r5 r6 rD r8 r< r9 r= c C sV || _ |d kr| j}|| _|| _|| _|| _|| _|| _|| _| | _ d | _ | � � d S )N) r3 rV r4 r5 r6 rD r8 r9 r< r= Zis_copy� _format_axes) �selfr3 r4 r5 r6 rD r8 r< r9 r= rR rR rS �__init__� s zWriter.__init__c C s t | ��d S )N)r )rX rR rR rS rW � s zWriter._format_axesc C s0 | j dk}t| j| j| j| j| j|| j| jd�S )N�iso)r4 r6 rD r8 � iso_datesr9 r= ) r5 �dumps� obj_to_writer4 r6 rD r8 r9 r= )rX r[ rR rR rS rL � s z Writer.writez"NDFrame | Mapping[IndexLabel, Any])�returnc C s dS )z Object to write in JSON format.NrR )rX rR rR rS r] � s zWriter.obj_to_write)Nr ) �__name__� __module__� __qualname__�__annotations__rY rW rL �propertyr r] rR rR rR rS rU � s rU c @ s* e Zd ZdZedd�dd��Zdd� ZdS ) rI r< z"NDFrame | Mapping[IndexLabel, Any])r^ c C s, | j s"| jdkr"| jj| jjd�S | jS d S )Nr? )rC �data)r< r4 r3 rC rB )rX rR rR rS r] � s zSeriesWriter.obj_to_writec C s* | j jjs&| jdkr&td| j� d���d S )Nr< z(Series index must be unique for orient='�')r3 r< � is_uniquer4 rF )rX rR rR rS rW � s zSeriesWriter._format_axesN)r_ r` ra rV rc r] rW rR rR rR rS rI � s rI c @ s* e Zd ZdZedd�dd��Zdd� ZdS ) rJ �columnsz"NDFrame | Mapping[IndexLabel, Any])r^ c C s0 | j s&| jdkr&| jjdd�}|d= n| j}|S )Nr? )r4 r< )r< r4 r3 �to_dict)rX r] rR rR rS r] � s zFrameWriter.obj_to_writec C sP | j jjs&| jdkr&td| j� d���| j jjsL| jdkrLtd| j� d���dS )z: Try to format axes if they are datelike. )r< rg z+DataFrame index must be unique for orient='z'.)r< rg rA z-DataFrame columns must be unique for orient='N)r3 r< rf r4 rF rg )rX rR rR rS rW � s zFrameWriter._format_axesN)r_ r` ra rV rc r] rW rR rR rR rS rJ � s rJ c sH e Zd ZdZdddddddddd �� fd d � Zed d �dd��Z� ZS )rH rA Nr z str | Noner0 r1 r2 z(Callable[[Any], JSONSerializable] | None)r4 r5 r6 rD r8 r< r9 r= c s. t � j||||||||| d� |dkr:d|� d�} t| ��t|| jd�| _|jdkrht|jt �rht d��|jdkr�|j t |jj �ks�t|j�|jj ��r�d } t| ��|�� }|jd gd �j} t| �r�|| �d d � �|| < t|jj�r�|j�� |_| j�s |jdd�| _n|jdd�| _d| _d| _|| _dS )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'. )r9 r= rZ z8Trying to write with `orient='table'` and `date_format='zH'`. Table Schema requires dates to be formatted with `date_format='iso'`)r< � z6orient='table' is not supported for MultiIndex columns� z/Overlapping names between the index and columns� timedelta)�includec S s | � � S )N)� isoformat)�xrR rR rS �<lambda>+ � z*JSONTableWriter.__init__.<locals>.<lambda>T)ZdropFrA N)�superrY rF r) r< �schema�ndimrG rg r rK rC �set�names�len� intersection�copyZ select_dtypesZapplymapr �dtypeZ to_timestampZ reset_indexr3 r5 r4 ) rX r3 r4 r5 r6 rD r8 r< r9 r= �msgZ timedeltas)� __class__rR rS rY � sD zJSONTableWriter.__init__z"NDFrame | Mapping[IndexLabel, Any])r^ c C s | j | jd�S )N)rr rd )rr r3 )rX rR rR rS r] 9 s z JSONTableWriter.obj_to_write)Nr )r_ r` ra rV rY rc r] � __classcell__rR rR )r{ rS rH � s ">rH r> )r> �numpy)Z old_arg_nameZ new_arg_namez2.0rN � )�versionZ allowed_args� stacklevel�frame�strictzDtypeArg | Nonez int | None) ry �keep_default_datesr} � precise_float�encoding_errorsr: � chunksizer; �nrowsr> c C s� |dkr|rt d��|dkr(|r(t d��|dkr<|dkr<d}|dkrP|dkrPd}t| ||||||||| | | | |||| d�}| r�|S |� |�� S Q R X dS )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``. orient : str 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 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. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. convert_axes : bool, default None Try to convert the axes to the proper dtypes. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. 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'``. numpy : bool, default False Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. .. deprecated:: 1.0.0 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. .. versionchanged:: 1.2 ``JsonReader`` is a context manager. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, bz2, zip or xz if path_or_buf is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. 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. .. versionadded:: 1.1 {storage_options} .. versionadded:: 1.2.0 Returns ------- Series or DataFrame 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. 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 -------- >>> 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(_, 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(_, 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(_, 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":"0.20.0"}},"data":[{{"index":"row 1","col 1":"a","col 2":"b"}},{{"index":"row 2","col 1":"c","col 2":"d"}}]}}' r@ z)cannot pass both dtype and orient='table'z0cannot pass both convert_axes and orient='table'NT)r4 �typry � convert_axes� convert_datesr� r} r� r8 �encodingr: r� r; r� r> r� )rF � JsonReader�read)rN r4 r� ry r� r� r� r} r� r8 r� r� r: r� r; r� r> Z json_readerrR rR rS � read_json> s@ r� c @ s~ e Zd ZdZd dddddddddd � d d �Zd d � Zdd� Zdd�dd�Zdd� Zdd� Z dd� Z dd� Z d d � Z d d � Z dS )!r� z� 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. Nr� r2 z int | Noner r z str | None) r� r} r� r: r� r; r� r> r� c C s� || _ || _|| _|| _|| _|| _|| _| | _| | _| | _ || _ || _ | | _ | | _ d| _|| _|| _d | _| j d k r�td| j d�| _ | j s�td��| jd k r�td| jd�| _| j s�td��| �|�}| �|�| _d S )Nr r� rj z*chunksize can only be passed if lines=Truer� z&nrows can only be passed if lines=True)r4 r� ry r� r� r� r} r� r8 r� r; r> r: r� � nrows_seenr� r� rQ r+ rF �_get_data_from_filepath�_preprocess_datard )rX �filepath_or_bufferr4 r� ry r� r� r� r} r� r8 r� r: r� r; r� r> r� rd rR rR rS rY r s8 zJsonReader.__init__c C sP t |d�r.| js.| js.| � |�� }W dQ R X t |d�sL| jsD| jrLt|�}|S )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. If self.chunksize, we prepare the data for the `__next__` method. Otherwise, we read it into memory for the `read` method. r� N)�hasattrr� r� r� r )rX rd rR rR rS r� � s zJsonReader._preprocess_datac C sT t |�}t|t�r*t|�s*t|�s*t|�rPt|d| j| j| j | j d�| _ | j j }|S )aL 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. �r)r� r; r> �errors) r' rG r0 r& r% r# r$ r� r; r> r� rQ rM )rX r� rR rR rS r� � s z"JsonReader._get_data_from_filepathr0 )r^ c C s& dd� dd� dd� |D �D ��� d�S )zG Combines a list of JSON objects into one JSON object. �[�,c s s | ]}|r|V qd S )NrR )�.0�linerR rR rS � <genexpr>� s z,JsonReader._combine_lines.<locals>.<genexpr>c s s | ]}|� � V qd S )N)�strip)r� r� rR rR rS r� � s �])�join)rX r: rR rR rS �_combine_lines� s zJsonReader._combine_linesc C s� | j rj| jrt| �}qv| jrDtt| j| j��}| �|�}| �|�}qvt | j�}|� d�}| �| �|��}n | �| j�}| � � |S )zA Read the whole JSON input into a pandas object. � ) r: r� r! r� �listr rd r� �_get_object_parserr r? �close)rX r3 r: � lines_jsonrd Z data_linesrR rR rS r� � s zJsonReader.readc C s� | j }| j}| j| j| j| j| j| j| j| jd�}d}|dkrNt |f|�� � }|dks^|dkr�t |t �sp||d< t |f|�� � }|S )z> Parses a json document into a pandas object. )r4 ry r� r� r� r} r� r8 Nr� Zseriesry )r� ry r4 r� r� r� r} r� r8 � FrameParser�parserG r2 � SeriesParser)rX �jsonr� ry �kwargsr3 rR rR rS r� � s$ z JsonReader._get_object_parserc C s | j dk r| j �� dS )z� If we opened a stream earlier, in _get_data_from_filepath, we should close it. If an open stream or file was passed, we leave it open. N)rQ r� )rX rR rR rS r� s zJsonReader.closec C s� | j r | j| j kr | �� t�tt| j| j��}|rv| �|�}| � |�}t | j| jt |� �|_ | jt |�7 _|S | �� t�d S )N) r� r� r� � StopIterationr� r rd r� r� r� �rangerv r< )rX r: r� r3 rR rR rS �__next__ s zJsonReader.__next__c C s | S )NrR )rX rR rR rS � __enter__) s zJsonReader.__enter__c C s | � � d S )N)r� )rX �exc_type� exc_value� tracebackrR rR rS �__exit__, s zJsonReader.__exit__)Nr� )r_ r` ra �__doc__rY r� r� r� r� r� r� r� r� r� rR rR rR rS r� i s " r� c @ s� e Zd ZU ded< ded< dZdddd d�Zd#d d�dd�Zdd� Zdd� Zdd� Z dd� Z dd� Z dd � Z d$d d �Z d d � Zd!d"� Zd S )%�Parserztuple[str, ...]� _split_keysr0 rV )rP r. �us�nsi�3�l ,b/ l `'�rl F[L'�NTFzDtypeArg | None)ry c C s� || _ |d kr| j}|| _|| _|dkr,d}| d k rd| �� } | | jkrVtd| j� ���| j| | _n | jd | _|| _ || _ || _ || _ | | _ || _d | _d S )Nr? Fzdate_unit must be one of rP )r� rV r4 ry �lower� _STAMP_UNITSrF � _MIN_STAMPS� min_stampr} r� r� r� r8 r� r3 ) rX r� r4 ry r� r� r� r} r� r8 rR rR rS rY < s( zParser.__init__c C s8 t |�� ��t | j��}|r4d�|�}td|� ���dS )zT Checks that dict has only the appropriate keys for orient='split'. z, z!JSON data had unexpected key(s): N)rt �keys� differencer� r� rF )rX �decodedZbad_keysZbad_keys_joinedrR rR rS �check_keys_splitd s zParser.check_keys_splitc C sF | j }|r| �� n| �� | jd kr*d S | jr8| �� | �� | jS )N)r} � _parse_numpy�_parse_no_numpyr3 r� � _convert_axes�_try_convert_types)rX r} rR rR rS r� m s z Parser.parsec C s t | ��d S )N)r )rX rR rR rS r� ~ s zParser._parse_numpyc C s t | ��d S )N)r )rX rR rR rS r� � s zParser._parse_no_numpyc C sT | j }|dk st�x<|jD ]2}| j||�|�ddd�\}}|rt| j ||� qW dS )z& Try to convert axes. NFT)rC rd � use_dtypesr� )r3 �AssertionErrorZ _AXIS_ORDERS�_try_convert_dataZ _get_axis�setattr)rX r3 Z axis_nameZnew_axis�resultrR rR rS r� � s zParser._convert_axesc C s t | ��d S )N)r )rX rR rR rS r� � s zParser._try_convert_typesc C s� |r�| j s.tt|��r |dfS |�tj�dfS | j dkr:n\t| j t�rR| j �|�n| j }|dk r�yt� |�}|� |�dfS t t fk r� |dfS X |r�| � |�\}}|r�|dfS |j dkr�y|� d�}W n t t fk r� Y nX |j j dk�r*|j dk�r*y|� d�}W n t t fk �r( Y nX t|��r�|j dk�sL|j dk�r�y |� d�}||k�� �rj|}W n t t tfk �r� Y nX |j d k�r�y|� d�}W n t t fk �r� Y nX |d k�r�t|��r�| jd k�r�|dfS |dfS ) zO Try to parse a ndarray like into a column by inferring dtype. FTN�objectZfloat64�f�float�int64r1 r< r? )ry �allr Zfillna�np�nanrG �dict�get�astype� TypeErrorrF �_try_convert_to_date�kindrv � OverflowErrorr4 )rX rC rd r� r� ry �new_datar� rR rR rS r� � sZ " zParser._try_convert_datac C s� t |�s|dfS |}|jdkrHy|�d�}W n tttfk rF Y nX t|jjtj �r�t |j �|| j kB |j t kB }|�� s�|dfS | jr�| jfn| j}xB|D ]:}yt|d|d�}W n tttfk r� w�Y nX |dfS W |dfS )z� Try to parse a ndarray like into a date column. Try to coerce object in epoch/iso formats and integer/float in epoch formats. Return a boolean if parsing was successful. Fr� r� �raise)r� �unitT)rv ry r� r� rF r� � issubclass�typer� �numberr Z_valuesr� r r� r8 r� r )rX rd r� Zin_rangeZ date_unitsr8 rR rR rS r� � s( zParser._try_convert_to_datec C s t | ��d S )N)r )rX rR rR rS �_try_convert_dates s zParser._try_convert_dates)NTTFFFN)TT)r_ r` ra rb r� r� rY r� r� r� r� r� r� r� r� r� rR rR rR rS r� 0 s. 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Nc S s dS )NTrR )�col�crR rR rS ro � rp z0FrameParser._process_converter.<locals>.<lambda>FT)r< )r3 r� � enumerater� r r< rg ) rX r� Zfiltr3 Z needs_new_objZnew_obj�ir� r� r� r� Z new_framerR rR rS �_process_converter� s" z FrameParser._process_converterc s2 � j d krd S � jr � �� � �� fdd�� d S )Nc s � j | |dd�S )NF)r� )r� )r� r� )rX rR rS ro � rp z0FrameParser._try_convert_types.<locals>.<lambda>)r3 r� r� r� )rX rR )rX rS r� � s z FrameParser._try_convert_typesc sZ �j d krd S �j� � dkr g � t� �� dd�dd�����fdd�� ��fdd�� d S ) NTr2 )r^ c S sT t | t�sdS | �� }|�d�sL|�d�sL|dksL|dksL|dksL|�d�rPdS dS ) zK Return if this col is ok to try for a date parse. FZ_at�_time�modified�date�datetime� timestampT)rG r0 r� �endswith� startswith)r� Z col_lowerrR rR rS �is_ok� s z-FrameParser._try_convert_dates.<locals>.is_okc s � � |�S )N)r� )r� r� )rX rR rS ro � rp z0FrameParser._try_convert_dates.<locals>.<lambda>c s �j r�| �p| � kS )N)r� )r� r� )r� r� rX rR rS ro � s )r3 r� rt r� )rX rR )r� r� rX rS r� � s z FrameParser._try_convert_dates)N) r_ r` ra rV r� r� r� r� r� r� rR rR rR rS r� C s & r� ) Nr, r- Tr. NFr/ Tr N)NNr� NNTTFFNNr� FNr/ NN)R� __future__r r r r � collectionsr� �ior � itertoolsr �typingr r r r} r� Zpandas._libs.jsonZ_libsr� Zpandas._libs.tslibsr Zpandas._typingr r r r r r Z pandas.errorsr Zpandas.util._decoratorsr r r Zpandas.core.dtypes.commonr r Zpandasr r r r r r Z pandas.corer Zpandas.core.constructionr Zpandas.core.genericr Zpandas.core.reshape.concatr! Zpandas.io.commonr" r# r$ r% r& r' Zpandas.io.json._normalizer( Z pandas.io.json._table_schemar) r* Zpandas.io.parsers.readersr+ r� r\ ZTABLE_SCHEMA_VERSIONrT rU rI rJ rH Z _shared_docsr� �Iteratorr� r� r� r� rR rR rR rS �<module> s� &17 P ( H d0
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