U ȽbK5@sUdZddlmZddlmZddlZddlZddlmZddl m Z m Z m Z m Z ddlZddlZddlmmZddlmZddlmZmZmZmZmZmZmZdd lmZm Z dd l!m"Z"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)m*Z*m+Z+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4m5Z5m6Z6ddl7m8Z8ddl9m:Z:m;Z;mZ>ddl?m@Z@mAZAdedBeCeddddZDddddddZEdd dd!ZFd"hZGd#d$hZHd"d$d%d&d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d#hZIGd8d9d9e ZJeJdd:eJdd:eJdd;eJdd<d=ZKd>eLd?<dd@dAZMdBdCZNdDdEdFdGZOe#ddHgdIdJe"eDjPdKdLdMe2dNe2dOdPejQddejQdddejQddddddddddddddddddddddddddQddRejRdddddSdddddeEd#dddf3dDdTdUdVdWdXdYdZd[dKZSe#ddHgdIdJe"eDjPd\d]d^e2dNe2dOdPejQddejQdddejQddddddddddddddddddddddddddQddRejRdddddSdddddeEd#dddf3dDdTdUdVdWdXdYdZd_d\ZTe#ddHgd`dJddDdadbdcdddedfdgZUGdhdidiejVZWdjdkZXddldmZYdndoZZdpdqZ[drdsdtdUdsdududvdwdxdydz d{d|Z\dyd}d~ddZ]dZ^dddddZ_ddydydddZ`dydd~ddZadS)zM Module contains tools for processing files into DataFrames or other objects ) annotations)abcN)fill)IOAnyCallable NamedTuple) STR_NA_VALUES) ArrayLikeCompressionOptions CSVEngineDtypeArgFilePath ReadCsvBufferStorageOptions)AbstractMethodError ParserWarning)Appenderdeprecate_nonkeyword_arguments)find_stack_level)validate_bool_kwarg) is_file_likeis_float is_integer is_list_like) DataFrame) RangeIndex) _shared_docs) IOHandles get_handlevalidate_header_arg)ArrowParserWrapper) ParserBase is_index_colparser_defaults)CParserWrapper)FixedWidthFieldParser PythonParseraI {summary} Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online docs for `IO Tools `_. Parameters ---------- filepath_or_buffer : 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, gs, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv. 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``. sep : str, default {_default_sep} Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python's builtin sniffer tool, ``csv.Sniffer``. In addition, separators longer than 1 character and different from ``'\s+'`` will be interpreted as regular expressions and will also force the use of the Python parsing engine. Note that regex delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``. delimiter : str, default ``None`` Alias for sep. header : int, list of int, None, default 'infer' Row number(s) to use as the column names, and the start of the data. Default behavior is to infer the column names: if no names are passed the behavior is identical to ``header=0`` and column names are inferred from the first line of the file, if column names are passed explicitly then the behavior is identical to ``header=None``. Explicitly pass ``header=0`` to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns e.g. [0,1,3]. Intervening rows that are not specified will be skipped (e.g. 2 in this example is skipped). Note that this parameter ignores commented lines and empty lines if ``skip_blank_lines=True``, so ``header=0`` denotes the first line of data rather than the first line of the file. names : array-like, optional List of column names to use. If the file contains a header row, then you should explicitly pass ``header=0`` to override the column names. Duplicates in this list are not allowed. index_col : int, str, sequence of int / str, or False, optional, default ``None`` Column(s) to use as the row labels of the ``DataFrame``, either given as string name or column index. If a sequence of int / str is given, a MultiIndex is used. Note: ``index_col=False`` can be used to force pandas to *not* use the first column as the index, e.g. when you have a malformed file with delimiters at the end of each line. usecols : list-like or callable, optional Return a subset of the columns. If list-like, all elements must either be positional (i.e. integer indices into the document columns) or strings that correspond to column names provided either by the user in `names` or inferred from the document header row(s). If ``names`` are given, the document header row(s) are not taken into account. For example, a valid list-like `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``. Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``. To instantiate a DataFrame from ``data`` with element order preserved use ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns in ``['foo', 'bar']`` order or ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]`` for ``['bar', 'foo']`` order. If callable, the callable function will be evaluated against the column names, returning names where the callable function evaluates to True. An example of a valid callable argument would be ``lambda x: x.upper() in ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster parsing time and lower memory usage. squeeze : bool, default False If the parsed data only contains one column then return a Series. .. deprecated:: 1.4.0 Append ``.squeeze("columns")`` to the call to ``{func_name}`` to squeeze the data. prefix : str, optional Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... .. deprecated:: 1.4.0 Use a list comprehension on the DataFrame's columns after calling ``read_csv``. mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. dtype : Type name or dict of column -> type, optional Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32, 'c': 'Int64'}} Use `str` or `object` together with suitable `na_values` settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : {{'c', 'python', 'pyarrow'}}, optional Parser engine to use. The C and pyarrow engines are faster, while the python engine is currently more feature-complete. Multithreading is currently only supported by the pyarrow engine. .. versionadded:: 1.4.0 The "pyarrow" engine was added as an *experimental* engine, and some features are unsupported, or may not work correctly, with this engine. converters : dict, optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels. true_values : list, optional Values to consider as True. false_values : list, optional Values to consider as False. skipinitialspace : bool, default False Skip spaces after delimiter. skiprows : list-like, int or callable, optional Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be ``lambda x: x in [0, 2]``. skipfooter : int, default 0 Number of lines at bottom of file to skip (Unsupported with engine='c'). nrows : int, optional Number of rows of file to read. Useful for reading pieces of large files. na_values : scalar, str, list-like, or dict, optional Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: 'z', 'Fz )subsequent_indentaS)'. keep_default_na : bool, default True Whether or not to include the default NaN values when parsing the data. Depending on whether `na_values` is passed in, the behavior is as follows: * If `keep_default_na` is True, and `na_values` are specified, `na_values` is appended to the default NaN values used for parsing. * If `keep_default_na` is True, and `na_values` are not specified, only the default NaN values are used for parsing. * If `keep_default_na` is False, and `na_values` are specified, only the NaN values specified `na_values` are used for parsing. * If `keep_default_na` is False, and `na_values` are not specified, no strings will be parsed as NaN. Note that if `na_filter` is passed in as False, the `keep_default_na` and `na_values` parameters will be ignored. na_filter : bool, default True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file. verbose : bool, default False Indicate number of NA values placed in non-numeric columns. skip_blank_lines : bool, default True If True, skip over blank lines rather than interpreting as NaN values. parse_dates : bool or list of int or names or list of lists or dict, default False The behavior is as follows: * boolean. If True -> try parsing the index. * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. * dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call result 'foo' If a column or index cannot be represented as an array of datetimes, say because of an unparsable value or a mixture of timezones, the column or index will be returned unaltered as an object data type. For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``. To parse an index or column with a mixture of timezones, specify ``date_parser`` to be a partially-applied :func:`pandas.to_datetime` with ``utc=True``. See :ref:`io.csv.mixed_timezones` for more. Note: A fast-path exists for iso8601-formatted dates. infer_datetime_format : bool, default False If True and `parse_dates` is enabled, pandas will attempt to infer the format of the datetime strings in the columns, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by 5-10x. keep_date_col : bool, default False If True and `parse_dates` specifies combining multiple columns then keep the original columns. date_parser : function, optional Function to use for converting a sequence of string columns to an array of datetime instances. The default uses ``dateutil.parser.parser`` to do the conversion. Pandas will try to call `date_parser` in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the string values from the columns defined by `parse_dates` into a single array and pass that; and 3) call `date_parser` once for each row using one or more strings (corresponding to the columns defined by `parse_dates`) as arguments. dayfirst : bool, default False DD/MM format dates, international and European format. cache_dates : bool, default True If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. .. versionadded:: 0.25.0 iterator : bool, default False Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. .. versionchanged:: 1.2 ``TextFileReader`` is a context manager. chunksize : int, optional Return TextFileReader object for iteration. See the `IO Tools docs `_ for more information on ``iterator`` and ``chunksize``. .. versionchanged:: 1.2 ``TextFileReader`` is a context manager. {decompression_options} .. versionchanged:: 1.4.0 Zstandard support. thousands : str, optional Thousands separator. decimal : str, default '.' Character to recognize as decimal point (e.g. use ',' for European data). lineterminator : str (length 1), optional Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored. quoting : int or csv.QUOTE_* instance, default 0 Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). doublequote : bool, default ``True`` When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate whether or not to interpret two consecutive quotechar elements INSIDE a field as a single ``quotechar`` element. escapechar : str (length 1), optional One-character string used to escape other characters. comment : str, optional Indicates remainder of line should not be parsed. If found at the beginning of a line, the line will be ignored altogether. This parameter must be a single character. Like empty lines (as long as ``skip_blank_lines=True``), fully commented lines are ignored by the parameter `header` but not by `skiprows`. For example, if ``comment='#'``, parsing ``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being treated as the header. encoding : str, optional Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python standard encodings `_ . .. versionchanged:: 1.2 When ``encoding`` is ``None``, ``errors="replace"`` is passed to ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``. This behavior was previously only the case for ``engine="python"``. .. versionchanged:: 1.3.0 ``encoding_errors`` is a new argument. ``encoding`` has no longer an influence on how encoding errors are handled. encoding_errors : str, optional, default "strict" How encoding errors are treated. `List of possible values `_ . .. versionadded:: 1.3.0 dialect : str or csv.Dialect, optional If provided, this parameter will override values (default or not) for the following parameters: `delimiter`, `doublequote`, `escapechar`, `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details. error_bad_lines : bool, optional, default ``None`` Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will be dropped from the DataFrame that is returned. .. deprecated:: 1.3.0 The ``on_bad_lines`` parameter should be used instead to specify behavior upon encountering a bad line instead. warn_bad_lines : bool, optional, default ``None`` If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. .. deprecated:: 1.3.0 The ``on_bad_lines`` parameter should be used instead to specify behavior upon encountering a bad line instead. on_bad_lines : {{'error', 'warn', 'skip'}} or callable, default 'error' Specifies what to do upon encountering a bad line (a line with too many fields). Allowed values are : - 'error', raise an Exception when a bad line is encountered. - 'warn', raise a warning when a bad line is encountered and skip that line. - 'skip', skip bad lines without raising or warning when they are encountered. .. versionadded:: 1.3.0 - callable, function with signature ``(bad_line: list[str]) -> list[str] | None`` that will process a single bad line. ``bad_line`` is a list of strings split by the ``sep``. If the function returns ``None``, the bad line will be ignored. If the function returns a new list of strings with more elements than expected, a ``ParserWarning`` will be emitted while dropping extra elements. Only supported when ``engine="python"`` .. versionadded:: 1.4.0 delim_whitespace : bool, default False Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be used as the sep. Equivalent to setting ``sep='\s+'``. If this option is set to True, nothing should be passed in for the ``delimiter`` parameter. low_memory : bool, default True Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference. To ensure no mixed types either set False, or specify the type with the `dtype` parameter. Note that the entire file is read into a single DataFrame regardless, use the `chunksize` or `iterator` parameter to return the data in chunks. (Only valid with C parser). memory_map : bool, default False If a filepath is provided for `filepath_or_buffer`, map the file object directly onto memory and access the data directly from there. Using this option can improve performance because there is no longer any I/O overhead. float_precision : str, optional Specifies which converter the C engine should use for floating-point values. The options are ``None`` or 'high' for the ordinary converter, 'legacy' for the original lower precision pandas converter, and 'round_trip' for the round-trip converter. .. versionchanged:: 1.2 {storage_options} .. versionadded:: 1.2 Returns ------- DataFrame or TextParser A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. See Also -------- DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.{func_name}('data.csv') # doctest: +SKIP FT)delim_whitespace na_filter low_memory memory_mapfloat_precisioninferd)colspecs infer_nrowswidths skipfooterr,r. chunksizecommentnrows thousandsr-dialectwarn_bad_lineserror_bad_lines on_bad_linesr*quotinglineterminator convertersdecimaliteratordayfirstinfer_datetime_formatverboseskipinitialspacec@seZdZUded<ded<dS)_DeprecationConfigr default_value str | NonemsgN)__name__ __module__ __qualname____annotations__rNrN=/tmp/pip-unpacked-wheel-t4uyudxd/pandas/io/parsers/readers.pyrFs rFzUse on_bad_lines in the future.z2Append .squeeze("columns") to the call to squeeze.z;Use a list comprehension on the column names in the future.)r;r:squeezeprefixzdict[str, _DeprecationConfig]_deprecated_defaultscCs^d|dd|d}|dk rZt|rBt||kr8t|t|}nt|rR||ksZt||S)a Checks whether the 'name' parameter for parsing is either an integer OR float that can SAFELY be cast to an integer without losing accuracy. Raises a ValueError if that is not the case. Parameters ---------- name : str Parameter name (used for error reporting) val : int or float The value to check min_val : int Minimum allowed value (val < min_val will result in a ValueError) 'sz' must be an integer >=dN)rint ValueErrorr)namevalZmin_valrIrNrNrOvalidate_integers  rZcCsH|dk rDt|tt|kr$tdt|ddsDt|tjsDtddS)aZ Raise ValueError if the `names` parameter contains duplicates or has an invalid data type. Parameters ---------- names : array-like or None An array containing a list of the names used for the output DataFrame. Raises ------ ValueError If names are not unique or are not ordered (e.g. set). Nz Duplicate names are not allowed.F)Z allow_setsz&Names should be an ordered collection.)lensetrWr isinstancerKeysView)namesrNrNrO_validate_namess  r`z4FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str])filepath_or_bufferc Cs|dddk r*|dddkr*d|d<n|dddkrBd|d<|dd}|dd}|dd kr|rttd |dk rtd ntd|ddd }|d d}t|ddt|f|}|s|r|S|||W5QRSQRXdS)zGeneric reader of line files. date_parserN parse_datesTFrAr5enginepyarrowz@The 'iterator' option is not supported with the 'pyarrow' enginezAThe 'chunksize' option is not supported with the 'pyarrow' enginer7r_)getrWrZr`TextFileReaderread)rakwdsrAr5r7parserrNrNrO_reads4     rlra)versionZ allowed_args stacklevelread_csvz8Read a comma-separated values (csv) file into DataFrame.z','storage_optionsdecompression_options) func_namesummaryZ _default_seprqrr."strictzDtypeArg | NoneCSVEngine | Noner strrHr)radtyperd compressionr@encoding_errorsrqc46 CsLt}4|4d=|4d=t|+||/| ||,|-|.||ddid }5|4|5t||4S)Nrasep delimiter,defaultslocalscopy_refine_defaults_readupdaterl6rar}r~headerr_ index_colusecolsrPrQmangle_dupe_colsrzrdr?Z true_valuesZ false_valuesrEskiprowsr4r7 na_valueskeep_default_nar+rDZskip_blank_linesrcrCZ keep_date_colrbrBZ cache_datesrAr5r{r8r@r> quotecharr= doublequote escapecharr6encodingr|r9r;r:r<r*r,r-r.rqrjZ kwds_defaultsrNrNrOrpHs$M   read_tablez+Read general delimited file into DataFrame.z'\\t' (tab-stop)c46 CsLt}4|4d=|4d=t|+||/| ||,|-|.||ddid }5|4|5t||4S)Nrar}r~ rrrrNrNrOrs$M  z"list[tuple[int, int]] | str | Nonezlist[int] | NonerVzDataFrame | TextFileReader)rar1r3r2returnc Ks.|dkr|dkrtdn|dkr2|dk r2td|dk rhgd}}|D]}||||f||7}qH|dk stt|d}|dk r t|t|kr |dkr d}|ddk r|d} | d k rt| sd }nt| }|d dkr t||t|kr td ||d <||d<d|d<t||S)a Read a table of fixed-width formatted lines into DataFrame. Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the `online docs for IO Tools `_. Parameters ---------- filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a text ``read()`` function.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.csv``. colspecs : list of tuple (int, int) or 'infer'. optional A list of tuples giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value 'infer' can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data which are not being skipped via skiprows (default='infer'). widths : list of int, optional A list of field widths which can be used instead of 'colspecs' if the intervals are contiguous. infer_nrows : int, default 100 The number of rows to consider when letting the parser determine the `colspecs`. **kwds : optional Optional keyword arguments can be passed to ``TextFileReader``. Returns ------- DataFrame or TextFileReader A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. See Also -------- DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. Examples -------- >>> pd.read_fwf('data.csv') # doctest: +SKIP Nz&Must specify either colspecs or widths)Nr/z4You must specify only one of 'widths' and 'colspecs'rr_r/rFrfrz-Length of colspecs must match length of namesr1r2 python-fwfrd)rWappendAssertionErrorrgr[rrl) rar1r3r2rjcolwr_Z len_indexrrNrNrOread_fwfs4:       &rc@seZdZdZd!dddddZdd Zd d Zd d ZddZddZ d"dddddZ ddZ d#ddZ d$ddZ ddZdd ZdS)%rhzF Passed dialect overrides any of the related parser options Nz;FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | listrx)frdcKs*|dk rd}nd}d}||_|d||_t|t|}|dk r\|dkrRtdt||}|dddkr|d dkr~d nd|d<||_d |_| |}|d d|d <| d d|_ | d d|_ | |||||\|_|_|j dd|_d|kr|d|jd<d|_|||j|_dS)NTpythonFengine_specifiedrez?The 'dialect' option is not supported with the 'pyarrow' enginerr/r_rrqr5r7rPZhas_index_names)rdrg_engine_specified_validate_skipfooter_extract_dialectrW_merge_with_dialect_properties orig_options_currow_get_options_with_defaultspopr5r7_check_file_or_buffer_clean_optionsoptionsrPhandles _make_engine_engine)selfrrdrjrr9rrNrNrO__init__ws:    zTextFileReader.__init__cCs"|jdk r|j|jdSN)rcloserrrNrNrOrs  zTextFileReader.closecCs|j}i}tD]\}}|||}|dkr|tkr||kr|t|d|kr|dkrj|ddk rjd}n|dkr|ddk rd}tdt|dq|dkr|d krtd q|||<qtD]\}}||krF||}|d krZ||krZd |kr |t kr n:|t |t |dj kr&ntdt|d t|dnt |t |dj }|||<q|dkrt D]\}}|||||<qv|S)Nrevaluer<r;r:The z2 option is not supported with the 'pyarrow' enginerFz3Setting mangle_dupe_cols=False is not supported yetcrz" option is not supported with the z enginer)rr$itemsrg_pyarrow_unsupportedgetattrrWrepr_c_parser_defaults_python_unsupportedrRrFrG _fwf_defaults)rrdrjrargnamedefaultrrNrNrOrsl        z)TextFileReader._get_options_with_defaultscCs&t|r"|dkr"t|ds"tddS)Nr__iter__z} |r|| t| krt d|dt| d|| =q|r0tjd|dttd|d} |d} |d } |d!}|d"}t|d#tD]b} t| t| }t| }|| ||jkrd$| d%|jd&}tj|ttdn||| <ql| d krt d't| rt| tttj fs| g} | |d<| dk r t| n| } | dk rPt| t!sTt"d(t#| j$ni} |d)}t%||\}}|d*krt&|s|dk rt d+n:t&|rtt'|}|dkrt(}nt)|st(|}| |d<| |d <||d!<||d,<||d"<|d-dkrdn|d-|d-<||fS).Nrr4rz*the 'c' engine does not support skipfooterrr~r*)rrezthe 'z>' engine does not support sep=None with delim_whitespace=Falserfz\s+T)rrzr' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex)zutf-8Fzthe separator encoded in z is > 1 char long, and the 'z)' engine does not support such separatorsrzNord(quotechar) > 127, meaning the quotechar is larger than one byte, and the 'z)' engine does not support such quotecharsz,Falling back to the 'python' engine because z, but this causes z= to be ignored as it is not supported by the 'python' engine.z;; you can avoid this warning by specifying engine='python'.rorr_r?rrrrzG argument has been deprecated and will be removed in a future version.  z)The value of index_col couldn't be 'True'z8Type converters must be a dict or subclass, input was a rrez@skiprows argument must be an integer when using engine='pyarrow' na_fvaluesrP)*rr[sysgetfilesystemencodingencodeUnicodeDecodeErrorr]rybytesordrrW_c_unsupportedrrrwarningswarnrrr rRkeysrgr$rGrI FutureWarningr#listtuplenpZndarraydict TypeErrortyperJ_clean_na_valuesrranger\callable)rrrdresultZfallback_reasonr}r*Z encodeablerrargrr_r?rrparser_defaultZ depr_defaultrIrrrNrNrOrs                             zTextFileReader._clean_optionscCs.z |WStk r(|YnXdSr) get_chunk StopIterationrrrNrNrO__next__s  zTextFileReader.__next__rz@FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str] | list | IOr c Csttttd}||kr0td|d|dt|tsd}d}|dkrRd}d }t|||j d d|j d d|j d d||j d d|j ddd|_ |j dk st |j j }n|dkrdt|}t|z|||f|j WStk r|j dk r|j YnXdS)N)rrrerzUnknown engine: z (valid options are )TrreFrbrr{r-r|rwrq)rr{r-is_texterrorsrqrz)Invalid file path or buffer object type: )r%r'r!r&rWrr]rrrrgrrhandler Exceptionr)rrrdmappingrmoderIrNrNrOrsH          zTextFileReader._make_enginecCs t|dSr)rrrNrNrO_failover_to_pythonsz"TextFileReader._failover_to_pythoncCs|jdkr:z|j}Wqtk r6|YqXntd|}z|j|\}}}Wntk rx|YnX|dkr|rttt| }t |j |j |}qd}nt|}t |||d}|j |7_ |j rt|jdkr| dS|S)Nrer7r)columnsindexrfr)rdrrirrrZr[nextitervaluesrrrrPrr)rr7ZdfrrZcol_dictZnew_rowsrNrNrOris.   zTextFileReader.readcCsF|dkr|j}|jdk r:|j|jkr(tt||j|j}|j|dS)N)r7)r5r7rrminri)rsizerNrNrOrs  zTextFileReader.get_chunkcCs|SrrNrrNrNrO __enter__szTextFileReader.__enter__cCs |dSr)r)rexc_type exc_value tracebackrNrNrO__exit__ szTextFileReader.__exit__)N)r)N)N)rJrKrL__doc__rrrrrrrrrirrrrNrNrNrOrhps  1C ( 1 ! rhcOsd|d<t||S)av Converts lists of lists/tuples into DataFrames with proper type inference and optional (e.g. string to datetime) conversion. Also enables iterating lazily over chunks of large files Parameters ---------- data : file-like object or list delimiter : separator character to use dialect : str or csv.Dialect instance, optional Ignored if delimiter is longer than 1 character names : sequence, default header : int, default 0 Row to use to parse column labels. Defaults to the first row. Prior rows will be discarded index_col : int or list, optional Column or columns to use as the (possibly hierarchical) index has_index_names: bool, default False True if the cols defined in index_col have an index name and are not in the header. na_values : scalar, str, list-like, or dict, optional Additional strings to recognize as NA/NaN. keep_default_na : bool, default True thousands : str, optional Thousands separator comment : str, optional Comment out remainder of line parse_dates : bool, default False keep_date_col : bool, default False date_parser : function, optional skiprows : list of integers Row numbers to skip skipfooter : int Number of line at bottom of file to skip converters : dict, optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the cell (not column) content, and return the transformed content. encoding : str, optional Encoding to use for UTF when reading/writing (ex. 'utf-8') squeeze : bool, default False returns Series if only one column. infer_datetime_format: bool, default False If True and `parse_dates` is True for a column, try to infer the datetime format based on the first datetime string. If the format can be inferred, there often will be a large parsing speed-up. float_precision : str, optional Specifies which converter the C engine should use for floating-point values. The options are `None` or `high` for the ordinary converter, `legacy` for the original lower precision pandas converter, and `round_trip` for the round-trip converter. .. versionchanged:: 1.2 rrd)rh)argsrjrNrNrO TextParsers8rcCs|dkr |rt}nt}t}nt|tr|}i}|D].\}}t|sT|g}|rdt|tB}|||<q>dd|D}n*t|s|g}t|}|r|tB}t|}||fS)NcSsi|]\}}|t|qSrN)_floatify_na_values).0kvrNrNrO bsz$_clean_na_values..) r r\r]rrrr_stringify_na_valuesr)rrrZ old_na_valuesrrrNrNrOrJs,   rc CsPt}|D]@}z t|}t|s,||Wq tttfk rHYq Xq |Sr)r\floatrisnanaddrrW OverflowError)rrrrNrNrOros rc Csg}|D]}|t|||zHt|}|t|kr`t|}||d|t|||Wntttfk rYnXz|t|Wqtttfk rYqXqt|S)z1return a stringified and numeric for these valuesz.0)rryrrVrrWrr\)rrxrrNrNrOr|s$  rzstr | csv.Dialectz str | objectboolz bool | Nonezstr | Callable | NonezArrayLike | None | objectzstr | None | objectzdict[str, Any]) r9r~r*rdr}r;r:r<r_rQrc Cs4| d} i} |dk r2|dko,|tjkp,|| k| d<|rH|tjk rHtd|dk rt|tjk rt| dk rt| tjk rttd|tjkrdn|| d<| tjkrdn| | d<|dkr|}|r|tjk rtd|d krtd |tjkr| | d<n|| d<|dk rd | d <nd | d<d| d <|dk r|dk s0|dk r8td|dkrPtjj| d<nf|dkrhtjj| d<nN|dkrtjj| d<n6t|r|dkrtd|| d<ntd|dnx|dk r$t |d|rtjj| d<nB|dk rt |d|rtjj| d<n tjj| d<n tjj| d<n tjj| d<| S)aPValidate/refine default values of input parameters of read_csv, read_table. Parameters ---------- dialect : str or csv.Dialect If provided, this parameter will override values (default or not) for the following parameters: `delimiter`, `doublequote`, `escapechar`, `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details. delimiter : str or object Alias for sep. delim_whitespace : bool Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be used as the sep. Equivalent to setting ``sep='\s+'``. If this option is set to True, nothing should be passed in for the ``delimiter`` parameter. engine : {{'c', 'python'}} Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. sep : str or object A delimiter provided by the user (str) or a sentinel value, i.e. pandas._libs.lib.no_default. error_bad_lines : str or None Whether to error on a bad line or not. warn_bad_lines : str or None Whether to warn on a bad line or not. on_bad_lines : str, callable or None An option for handling bad lines or a sentinel value(None). names : array-like, optional List of column names to use. If the file contains a header row, then you should explicitly pass ``header=0`` to override the column names. Duplicates in this list are not allowed. prefix : str, optional Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... defaults: dict Default values of input parameters. Returns ------- kwds : dict Input parameters with correct values. Raises ------ ValueError : If a delimiter was specified with ``sep`` (or ``delimiter``) and ``delim_whitespace=True``. If on_bad_lines is specified(not ``None``) and ``error_bad_lines``/ ``warn_bad_lines`` is True. r~N sep_overridez:Specified a sep and a delimiter; you can only specify one.z5Specified named and prefix; you can only specify one.r_rQzXSpecified a delimiter with both sep and delim_whitespace=True; you can only specify one. zSpecified \n as separator or delimiter. This forces the python engine which does not accept a line terminator. Hence it is not allowed to use the line terminator as separator.TrrrdFz[Both on_bad_lines and error_bad_lines/warn_bad_lines are set. Please only set on_bad_lines.errorr<rskiprz>on_bad_line can only be a callable function if engine='python'z Argument z is invalid for on_bad_linesr;r:) lib no_defaultrWr"ZBadLineHandleMethodERRORWARNZSKIPrr) r9r~r*rdr}r;r:r<r_rQrZ delim_defaultrjrNrNrOrsA                 rzcsv.Dialect | None)rjrcCs<|ddkrdS|d}|tkr0t|}t||S)za Extract concrete csv dialect instance. Returns ------- csv.Dialect or None r9N)rgcsv list_dialects get_dialect_validate_dialect)rjr9rNrNrOrGs  r)r~rrrErr=z csv.DialectNone)r9rcCs(tD]}t||std|dqdS)zx Validate csv dialect instance. Raises ------ ValueError If incorrect dialect is provided. zInvalid dialect z providedN)MANDATORY_DIALECT_ATTRSrrW)r9paramrNrNrOres  r)r9rrc Cs|}tD]}t||}t|}|||}g}||krx||krxd|d|d|d}|dkrn|ddsx|||rtjd |t t d |||<q |S) a Merge default kwargs in TextFileReader with dialect parameters. Parameters ---------- dialect : csv.Dialect Concrete csv dialect. See csv.Dialect documentation for more details. defaults : dict Keyword arguments passed to TextFileReader. Returns ------- kwds : dict Updated keyword arguments, merged with dialect parameters. zConflicting values for 'z': 'z+' was provided, but the dialect specifies 'z%'. Using the dialect-specified value.r~rFrr) rrrr$rgrrrrjoinrr) r9rrjrZ dialect_valrprovidedZ conflict_msgsrIrNrNrOrss&     rcCs<|dr8|ds|dr&td|dr8tddS)a Check whether skipfooter is compatible with other kwargs in TextFileReader. Parameters ---------- kwds : dict Keyword arguments passed to TextFileReader. Raises ------ ValueError If skipfooter is not compatible with other parameters. r4rAr5z('skipfooter' not supported for iterationr7z''skipfooter' not supported with 'nrows'N)rgrW)rjrNrNrOrs   r)r)r/Nr0)T)br __future__r collectionsrr rtextwraprtypingrrrrrZnumpyrZpandas._libs.libZ_libsrZpandas._libs.parsersr Zpandas._typingr r r r rrrZ pandas.errorsrrZpandas.util._decoratorsrrZpandas.util._exceptionsrZpandas.util._validatorsrZpandas.core.dtypes.commonrrrrZpandas.core.framerZpandas.core.indexes.apirZpandas.core.shared_docsrZpandas.io.commonrrr Z&pandas.io.parsers.arrow_parser_wrapperr!Zpandas.io.parsers.base_parserr"r#r$Z"pandas.io.parsers.c_parser_wrapperr%Zpandas.io.parsers.python_parserr&r'rsortedZ_doc_read_csv_and_tablerrrrrrFrRrMrZr`rlformatr  QUOTE_MINIMALrprrIteratorrhrrrrrrrrrrrNrNrNrOs    $        l  -  W  W_!< % "3 6