B ®@`F!ã2@søUdZddlmZmZddlZddlZddlmZddlZddl Z ddl Z ddl m Z ddl mZmZmZmZmZmZmZmZmZmZddlZddlZddlmmZddlmmZ ddl!mm"Z"ddl!m#Z#ddl$m%Z%dd l&m'Z'm(Z(m)Z)dd l*m+Z+m,Z,m-Z-m.Z.dd l/m0Z0dd l1m2Z2dd l3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAmBZBmCZCddlDmEZEddlFmGZGddlHmIZImJZJddlKmLZLddlMmNZNddlOmPZPmQZQmRZRmSZSddlTmUZUddlVmWZXddlYmZZZm[Z[m\Z\ddl]m^Z^dZ_de d `eae#ƒ¡ddd�dZbd…dd „Zcd!d"„Zde'd#œd$d%„Zeddd&ejfd'd(dd)dddddddd'ddddd'ddd*d(d(d(dddd(dd(dd'd(d'd+œ%Zgd(d'd'd(d'd'dd,œZhd)d-dd.œZid/hZjd0d1hZkiZleemefend2<eoƒZpeemend3<e0ebjqd4d5d6eJjrd7d8�ƒejsdd)dddd(dd'dddddd(ddddd'd'd(d'd(d(d(dd(d'd(dd)dd*dd&ejfd'ddddd'd'd(ehd0d(ddf1e'eme(d9œd:d4„ƒZte0ebjqd;dœd?d;„ƒZud†e'd#œd@dA„ZvGdBdC„dCejƒZwdDdE„Zxd‡ee)eyeezfdFœdGdH„Z{dIdJ„Z|dKdL„Z}dMdN„Z~dOdP„ZdQdR„Z€GdSdT„dTƒZ�GdUdV„dVe�ƒZ‚dWdX„ZƒezdYœdZd[„Z„Gd\d]„d]e�ƒZ…dˆd^d_„Z†d‰d`da„Z‡dbdc„ZˆdŠddde„Z‰dfdg„ZŠd‹dhdi„Z‹djdk„ZŒdldm„Z�dndo„ZŽdpdq„Z�Gdrds„dsejƒZ�Gdtdu„due…ƒZ‘e)emej’fe)eme“feyeme)eme“feemefdvœdwdx„Z”eemefeej’dyœdzd{„Z•d|Z–ej’dd}œd~d„Z—ej’eemefeemefd€œd�d‚„Z˜eemefddyœdƒd„„Z™dS)ŒzM Module contains tools for processing files into DataFrames or other objects é)ÚabcÚ defaultdictN)ÚStringIO)Úfill) ÚAnyÚDictÚIterableÚIteratorÚListÚOptionalÚSequenceÚSetÚTypeÚcast)Ú STR_NA_VALUES)Úparsing)ÚFilePathOrBufferÚStorageOptionsÚUnion)ÚAbstractMethodErrorÚEmptyDataErrorÚ ParserErrorÚ ParserWarning)ÚAppender)Úastype_nansafe)Ú ensure_objectÚ ensure_strÚ is_bool_dtypeÚis_categorical_dtypeÚ is_dict_likeÚis_dtype_equalÚis_extension_array_dtypeÚ is_file_likeÚis_floatÚ is_integerÚis_integer_dtypeÚ is_list_likeÚis_object_dtypeÚ is_scalarÚis_string_dtypeÚ pandas_dtype)ÚCategoricalDtype)Úisna)Ú algorithmsÚgeneric)Ú Categorical)Ú DataFrame)ÚIndexÚ MultiIndexÚ RangeIndexÚensure_index_from_sequences)ÚSeries)Ú datetimes)Ú IOHandlesÚ get_handleÚvalidate_header_arg)Úgeneric_parseruaÜ {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, 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, 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). 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. prefix : str, optional Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... 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'}}, optional Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. 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', 'éFz )Úsubsequent_indenta#'. 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. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and `filepath_or_buffer` is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no decompression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. 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 `_ . 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, default True 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 dropped from the DataFrame that is returned. warn_bad_lines : bool, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. 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 cCs^d|d›d|d›�}|dk rZt|ƒrBt|ƒ|kr8t|ƒ‚t|ƒ}nt|ƒrR||ksZt|ƒ‚|S)aÀ 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 : string 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) ú'Úsz' must be an integer >=ÚdN)r#ÚintÚ ValueErrorr$)ÚnameÚvalZmin_valÚmsg©rEú5/tmp/pip-unpacked-wheel-q9tj5l6a/pandas/io/parsers.pyÚvalidate_integer…s  rGcCsH|dk rDt|ƒtt|ƒƒkr$tdƒ‚t|dd�sDt|tjƒsDtdƒ‚dS)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.)ÚlenÚsetrAr&Ú isinstancerÚKeysView)ÚnamesrErErFÚ_validate_names¢s rM)Úfilepath_or_bufferc Cs”| dd¡dk r&t|dtƒr&d|d<| dd¡}td| dd¡dƒ}| d d¡}t| d d¡ƒt|f|Ž}|sv|rz|S|� | |¡SQRXdS) zGeneric reader of line files.Ú date_parserNÚ parse_datesTÚiteratorFÚ chunksizeéÚnrowsrL)ÚgetrJÚboolrGrMÚTextFileReaderÚread)rNÚkwdsrQrRrTÚparserrErErFÚ_readºs   r[ú"TFÚinferÚ.)%Ú delimiterÚ escapecharÚ quotecharÚquotingÚ doublequoteÚskipinitialspaceÚlineterminatorÚheaderÚ index_colrLÚprefixÚskiprowsÚ skipfooterrTÚ na_valuesÚkeep_default_naÚ true_valuesÚ false_valuesÚ convertersÚdtypeÚ cache_datesÚ thousandsÚcommentÚdecimalrPÚ keep_date_colÚdayfirstrOÚusecolsrRÚverboseÚencodingÚsqueezeÚ compressionÚmangle_dupe_colsÚinfer_datetime_formatÚskip_blank_lines)Údelim_whitespaceÚ na_filterÚ low_memoryÚ memory_mapÚerror_bad_linesÚwarn_bad_linesÚfloat_precisionéd)ÚcolspecsÚ infer_nrowsÚwidthsrjr�r…Ú_deprecated_defaultsÚ_deprecated_argsÚread_csvz8Read a comma-separated values (csv) file into DataFrame.z','Ústorage_options)Ú func_nameÚsummaryZ _default_sepr�)rNrtr�c24Cs>tƒ}2|2d=|2d=t|*||-| |ddid�}3|2 |3¡t||2ƒS)NrNÚsepr_ú,)Údefaults)ÚlocalsÚ_refine_defaults_readÚupdater[)4rNr�r_rfrLrgrwrzrhr|rpÚenginerormrnrdrirjrTrkrlr€rxr~rPr}rurOrvrqrQrRr{rrrtrerarbrcr`rsryÚdialectrƒr„rr�r‚r…r�rYÚ kwds_defaultsrErErFrŒsD Ú read_tablez+Read general delimited file into DataFrame.z'\\t' (tab-stop))rNrtc13Cs>tƒ}1|1d=|1d=t|*||-| |ddid�}2|1 |2¡t||1ƒS)NrNr�r_ú )r’)r“r”r•r[)3rNr�r_rfrLrgrwrzrhr|rpr–rormrnrdrirjrTrkrlr€rxr~rPr}rurOrvrqrQrRr{rrrtrerarbrcr`rsryr—rƒr„rr�r‚r…rYr˜rErErFr™`sC cKsŽ|dkr|dkrtdƒ‚n|dkr2|dk r2tdƒ‚|dk rlgd}}x&|D]}| |||f¡||7}qJW||d<||d<d|d <t||ƒS) aW 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 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.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``. 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`. .. versionadded:: 0.24.0 **kwds : optional Optional keyword arguments can be passed to ``TextFileReader``. 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. 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Parameters ---------- columns : array-like Object which may or may not be convertible into a MultiIndex index_col : None, bool or list, optional Column or columns to use as the (possibly hierarchical) index Returns ------- boolean : Whether or not columns could become a MultiIndex Nc3s$|]}|tˆƒkrt|tƒVqdS)N)rÑrJrÒ)Ú.0r³)rgrErFú \sz,_is_potential_multi_index..)rJrVrHr2Úall)rèrgrE)rgrFÚ_is_potential_multi_indexDs  rücs"tˆƒr‡fdd„t|ƒDƒSˆS)zó Check whether or not the 'usecols' parameter is a callable. If so, enumerates the 'names' parameter and returns a set of indices for each entry in 'names' that evaluates to True. If not a callable, returns 'usecols'. csh|]\}}ˆ|ƒr|’qSrErE)rùÚirB)rwrErFú isz$_evaluate_usecols..)rÛÚ enumerate)rwrLrE)rwrFÚ_evaluate_usecols`srcs0‡fdd„|Dƒ}t|ƒdkr,td|›�ƒ‚|S)a% Validates that all usecols are present in a given list of names. If not, raise a ValueError that shows what usecols are missing. Parameters ---------- usecols : iterable of usecols The columns to validate are present in names. names : iterable of names The column names to check against. Returns ------- usecols : iterable of usecols The `usecols` parameter if the validation succeeds. Raises ------ ValueError : Columns were missing. Error message will list them. csg|]}|ˆkr|‘qSrErE)rùr³)rLrErFú ƒsz+_validate_usecols_names..rz>Usecols do not match columns, columns expected but not found: )rHrA)rwrLÚmissingrE)rLrFÚ_validate_usecols_namesms   rcCs$t|ƒstdƒ‚|dkr tdƒ‚|S)að Validate the 'skipfooter' parameter. Checks whether 'skipfooter' is a non-negative integer. Raises a ValueError if that is not the case. Parameters ---------- skipfooter : non-negative integer The number of rows to skip at the end of the file. Returns ------- validated_skipfooter : non-negative integer The original input if the validation succeeds. 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Parameters ---------- col : str The name of the column. na_values : array-like, dict The object listing the NaN values as strings. na_fvalues : array-like, dict The object listing the NaN values as floats. keep_default_na : bool If `na_values` is a dict, and the column is not mapped in the dictionary, whether to return the default NaN values or the empty set. Returns ------- nan_tuple : A length-two tuple composed of 1) na_values : the string NaN values for that column. 2) na_fvalues : the float NaN values for that column. N)rJrÕrrI)rœrkrÃrlrErErFrF s  rFcCsJt|ƒ}g}x8|D]0}||kr*| |¡qt|tƒr| ||¡qW|S)N)rIr›rJr@)rèrèrírîr³rErErFÚ_get_col_names,s   röc@s6eZdZdZd dd„Zd dd„Zddd „Zd d „ZdS)ÚFixedWidthReaderz( A reader of fixed-width lines. Nr†cCsÒ||_d|_|rd|nd|_||_|dkr>|j||d�|_n||_t|jttfƒsht dt |ƒj ›�ƒ‚xd|jD]Z}t|ttfƒrÂt |ƒdkrÂt|dt tjt dƒfƒrÂt|dt tjt dƒfƒspt d ƒ‚qpWdS) Nz z r])rˆriz;column specifications must be a list or tuple, input was a rÂrrSzEEach column specification must be 2 element tuple or list of integers)r¢Úbufferr_rsÚdetect_colspecsr‡rJrÒrÑrÖr×rØrHr@rÓr)r°r¢r‡r_rsrirˆrèrErErFr±<s$  zFixedWidthReader.__init__cCsf|dkrtƒ}g}g}x@t|jƒD]2\}}||kr<| |¡| |¡t|ƒ|kr"Pq"Wt|ƒ|_|S)aÉ Read rows from self.f, skipping as specified. We distinguish buffer_rows (the first <= infer_nrows lines) from the rows returned to detect_colspecs because it's simpler to leave the other locations with skiprows logic alone than to modify them to deal with the fact we skipped some rows here as well. Parameters ---------- infer_nrows : int Number of rows to read from self.f, not counting rows that are skipped. skiprows: set, optional Indices of rows to skip. Returns ------- detect_rows : list of str A list containing the rows to read. N)rIrÿr¢r›rHrërø)r°rˆriZ buffer_rowsZ detect_rowsrýrÕrErErFÚget_rowsZs    zFixedWidthReader.get_rowsc sd dd„ˆjDƒ¡}t d|›d�¡}ˆ ||¡}|s@tdƒ‚ttt|ƒƒ}t j |dt d�}ˆj dk r|‡fd d „|Dƒ}x4|D],}x&|  |¡D]} d||  ¡|  ¡…<q’Wq‚Wt  |d¡} d | d <t  || Adk¡d } tt| ddd …| ddd …ƒƒ} | S) Nrcss|]}d|›�VqdS)ú\NrE)rùr+rErErFrú‚sz3FixedWidthReader.detect_colspecs..z([^z]+)z(No rows from which to infer column widthrS)rpcsg|]}| ˆj¡d‘qS)r)Ú partitionrs)rùrÕ)r°rErFrŠsz4FixedWidthReader.detect_colspecs..rrÂ)r r_r“r”rúrr×rrHrÓÚzerosr@rsÚfinditerr»r½ZrollÚwhererÑr-) r°rˆriÚ delimitersÚpatternr«rÚrUrÕÚmZshiftedÚedgesZ edge_pairsrE)r°rFrù€s"    "z FixedWidthReader.detect_colspecscs`ˆjdk r@ytˆjƒ‰WqJtk r<dˆ_tˆjƒ‰YqJXn tˆjƒ‰‡‡fdd„ˆjDƒS)Ncs$g|]\}}ˆ||… ˆj¡‘qSrE)rœr_)rùZfrommÚto)r�r°rErFržsz-FixedWidthReader.__next__..)rørêràr¢r‡)r°rE)r�r°rFr½”s  zFixedWidthReader.__next__)Nr†)N)r†N)rØrör÷rør±rúrùr½rErErErFr÷7s   & r÷c@s.eZdZdZdd„Zdd„Zedœdd„Zd S) räzl Specialization that Converts fixed-width fields into DataFrames. See PythonParser for details. cKs,| d¡|_| d¡|_tj||f|ŽdS)Nr‡rˆ)rªr‡rˆrãr±)r°r¢rYrErErFr±§s  zFixedWidthFieldParser.__init__cCs"t||j|j|j|j|jƒ|_dS)N)r÷r‡r_rsrirˆr:)r°r¢rErErFrŠ­sz"FixedWidthFieldParser._make_reader)rcCsdd„|DƒS)z¿ Returns the list of lines without the empty ones. With fixed-width fields, empty lines become arrays of empty strings. See PythonParser._remove_empty_lines. cSs"g|]}tdd„|Dƒƒr|‘qS)css"|]}t|tƒ p| ¡VqdS)N)rJrÉrœ)rùrÊrErErFrúÁszGFixedWidthFieldParser._remove_empty_lines...)r)rùr�rErErFr¿sz=FixedWidthFieldParser._remove_empty_lines..rE)r°r¥rErErFr·sz)FixedWidthFieldParser._remove_empty_linesN)rØrör÷rør±rŠr rÂrErErErFrä¡s rä)r—r_rr–r�r’cCs–|d}i}|dk r2|dko,|tjkp,||k|d<|dkr>|}|rT|tjk rTtdƒ‚|tjkrh||d<n||d<|dk r‚d|d<nd|d<d |d<|S) arValidate/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. 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``. r_NÚ sep_overridezXSpecified a delimiter with both sep and delim_whitespace=True; you can only specify one.Tr r³r–F)r Ú no_defaultrA)r—r_rr–r�r’Z delim_defaultrYrErErFr”Ås$-    r”)rYrcCs<| d¡dkrdS|d}|t ¡kr0t |¡}t|ƒ|S)za Extract concrete csv dialect instance. Returns ------- csv.Dialect or None r—N)rUr‹Ú list_dialectsÚ get_dialectÚ_validate_dialect)rYr—rErErFr¥s  r¥)r_rcr`rdrarb)r—rcCs,x&tD]}t||ƒstd|›d�ƒ‚qWdS)zx Validate csv dialect instance. Raises ------ ValueError If incorrect dialect is provided. zInvalid dialect z providedN)ÚMANDATORY_DIALECT_ATTRSr¾rA)r—ÚparamrErErFr <s  r )r—r’rc Cs¤| ¡}x–tD]Ž}t||ƒ}t|}| ||¡}g}||krz||krzd|›d|›d|›d�}|dkrp| dd¡sz| |¡|r”tjd  |¡t d d �|||<qW|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_rFz rÂ)rÁ) rÄr Úgetattrr´rUrªr›rÍrÎr r) r—r’rYr Z dialect_valrÞÚprovidedZ conflict_msgsrDrErErFr¦Js     r¦cCs<| d¡r8| d¡s| d¡r&tdƒ‚| d¡r8tdƒ‚dS)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. rjrQrRz('skipfooter' not supported for iterationrTz''skipfooter' not supported with 'nrows'N)rUrA)rYrErErFr¤~s   r¤)r)r]Nr†)N)NFFT)F)T)N)šrøÚ collectionsrrr‹räÚiorrr“rÅÚtextwraprÚtypingrrrr r r r r rrrÍZnumpyrÓZpandas._libs.libZ_libsr Zpandas._libs.opsÚopsr]Zpandas._libs.parsersr\rZpandas._libs.tslibsrZpandas._typingrrrZ pandas.errorsrrrrZpandas.util._decoratorsrZpandas.core.dtypes.castrZpandas.core.dtypes.commonrrrrrr r!r"r#r$r%r&r'r(r)r*Zpandas.core.dtypes.dtypesr+Zpandas.core.dtypes.missingr,Z pandas.corer-r.Zpandas.core.arraysr/Zpandas.core.framer0Zpandas.core.indexes.apir1r2r3r4Zpandas.core.seriesr5Zpandas.core.toolsr6râZpandas.io.commonr7r8r9Zpandas.io.date_convertersr:rºr r!Z_doc_read_csv_and_tablerGrMr[Ú QUOTE_MINIMALr´r¶r¹rÌr·rŠrÉÚ__annotations__rIr‹ÚformatZ _shared_docsrrŒr™ržrWrÐrVr@rürrrr r r râr~r�rãrrcrêrÙr,rxrïrðrFrör÷rärŸròr”r¥r r r¦r¤rErErErFÚsú  0     H       x6  MB  2IB<0 3 > ,$ , # j%   S  1