B x\V&@sdZddlmZddlmZddlZddlZddlZddlZddl m Z ddl Z ddl Z ddlmmZddlmmZddlmmZddlmZddlmZddlmZmZmZmZmZmZm Z m!Z!m"Z"ddl#m$Z$m%Z%m&Z&m'Z'dd l(m)Z)dd l*m+Z+dd l,m-Z-m.Z.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7m8Z8m9Z9dd l:m;Z;dd lm?Z?ddl@mAZAddlBmCZCddlDmEZEmFZFmGZGmHZHddlImJZJddlKmLZMddlNmOZOmPZPmQZQmRZRmSZSmTZTmUZUmVZVmWZWddlXmYZYe!dZZde d[e\eOddddZ]drddZ^dd Z_d!d"Z`ddd#ejad$d%dd&dddddddd$ddddddd'd%d%d%dddd%dd%dd$d%d%d$d(%Zbd%d$d$d%d$d$d%dd)Zcd&d*dd+Zdd,hZed-d.hZfd/diZgd/hZhdsd1d2Zieid3d0d4Zje)e]jkd3d5d6d7ejZjeid8d9d4Zle)e]jkd8d:d;d7elZldtdd?d?ePZnd@dAZodBdCZpdDdEZqdFdGZrdHdIZsdJdKZtdLdMZuGdNdOdOevZwGdPdQdQewZxdRdSZydTdUZzGdVdWdWewZ{dudXdYZ|dvdZd[Z}d\d]Z~dwd^d_Zd`daZdxdbdcZdddeZdfdgZdhdiZdjdkZdldmZGdndodoePZGdpdqdqe{ZdS)yzM Module contains tools for processing files into DataFrames or other objects )print_function) defaultdictN)fill)parsing) PY3StringIOlrangelzipmaprange string_typesuzip)AbstractMethodErrorEmptyDataError ParserError ParserWarning)Appender)astype_nansafe) ensure_object is_bool_dtypeis_categorical_dtypeis_dtype_equalis_extension_array_dtypeis_float is_integeris_integer_dtype is_list_likeis_object_dtype is_scalaris_string_dtype pandas_dtype)CategoricalDtype)isna) algorithms) Categorical) DataFrame)Index MultiIndex RangeIndexensure_index_from_sequences)Series) datetimes) _NA_VALUES BaseIterator UnicodeReader UTF8Recoder _get_handle_infer_compression_validate_header_argget_filepath_or_buffer is_file_like)generic_parserua {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, 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 either ``pathlib.Path`` or ``py._path.local.LocalPath``. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (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 file contains no header row, then you should explicitly pass ``header=None``. Duplicates in this list will cause a ``UserWarning`` to be issued. index_col : int, sequence or bool, optional Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider ``index_col=False`` to force pandas to not use the first column as the index (row names). 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', 'Fz )Zsubsequent_indentat"'. 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 unparseable 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. iterator : bool, default False Return TextFileReader object for iteration or getting chunks with ``get_chunk()``. chunksize : int, optional Return TextFileReader object for iteration. See the `IO Tools docs `_ for more information on ``iterator`` and ``chunksize``. 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. .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression. 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. tupleize_cols : bool, default False Leave a list of tuples on columns as is (default is to convert to a MultiIndex on the columns). .. deprecated:: 0.21.0 This argument will be removed and will always convert to MultiIndex 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. .. versionadded:: 0.18.1 support for the Python parser. 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` for the ordinary converter, `high` for the high-precision converter, and `round_trip` for the round-trip converter. Returns ------- DataFrame or TextParser A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes. See Also -------- 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 cCsXdj||d}|dk rTt|r={min_val:d})namemin_valN)formatrint ValueErrorr)r8valr9msgr?0lib/python3.7/site-packages/pandas/io/parsers.py_validate_integer[s  rAcCs4|dk r0t|tt|kr0d}tj|tdd|S)am Check if the `names` parameter contains duplicates. If duplicates are found, we issue a warning before returning. Parameters ---------- names : array-like or None An array containing a list of the names used for the output DataFrame. Returns ------- names : array-like or None The original `names` parameter. NzBDuplicate names specified. This will raise an error in the future.) stacklevel)lensetwarningswarn UserWarning)namesr>r?r?r@_validate_namesys rJc Cs"|dd}|dk r.tdd|}||d<|dd}t||}t|||\}}}}||d<|dddk rt|dtrd |d<|d d }td |d dd }|dd}t |ddt |f|} |s|r| Sz| |} Wd| X|ry | Wnt k rYnX| S)zGeneric reader of line files.encodingN_- compressioninfer date_parser parse_datesTiteratorF chunksizenrowsrI)getresublowerr2r4 isinstanceboolrArJTextFileReaderreadcloser<) filepath_or_bufferkwdsrKrNrLZ should_closerRrSrUparserdatar?r?r@_reads8        rc"TFrO.)% delimiter escapechar quotecharquoting doublequoteskipinitialspacelineterminatorheader index_colrIprefixskiprows skipfooterrU na_valueskeep_default_na true_values false_values convertersdtype thousandscommentdecimalrQ keep_date_coldayfirstrPusecolsrSverboserKsqueezerNmangle_dupe_cols tupleize_colsinfer_datetime_formatskip_blank_lines)delim_whitespace na_filter low_memory memory_maperror_bad_lineswarn_bad_linesrfloat_precisiond)colspecs infer_nrowswidthsrqrrr,c0sdkrd}n}|ddddddddddddddddddddddddddddddddddtjdddddddddtdddf0fd d }|_|S) N read_tableFrOTrrerdrc142sdkrF|dkr*|dkr*tjdtddntjdtdd|dkrF}|)dk rj|dko\|k}1t|1d}2nt}2|dkr||}|-r|krtd| dk rd }3nd } d}3|2j|| |)||3|%|&|#|$||"|||||||| ||| |'|!|||||||| | |||(||/|0||-|,|+|.| |*||d 0t||2S) NrFzAread_table is deprecated, use read_csv instead, passing sep='\t'.)rCz/read_table is deprecated, use read_csv instead.) sep_overridezXSpecified a delimiter with both sep and delim_whitespace=True; you can only specify one.Tc)0rfenginedialectrNengine_specifiedrjrgrhrirkrlrmrnrIrorprqrrrtrursrxryrzrQr{r|rPrUrRrSrvrwr}r~rKrrrrrrrrrrrr)rFrG FutureWarningdictr<updaterc)4r_seprfrmrIrnr}rrorrwrrvrtrurkrprqrUrrrsrr~rrQrr{rPr|rRrSrNrxrzrlrhrirjrgryrKrrrrrrrrrr`r) default_sepr8r?r@parser_fsC    z'_make_parser_function..parser_f)csv QUOTE_MINIMAL_c_parser_defaults__name__)r8rrrr?)rr8r@_make_parser_functionsjhrread_csv)rz8Read a comma-separated values (csv) file into DataFrame.z',')Z func_nameZsummaryZ _default_sepr zRead general delimited file into DataFrame. .. deprecated:: 0.24.0 Use :func:`pandas.read_csv` instead, passing ``sep='\t'`` if necessary.z'\\t' (tab-stop)cKs|dkr|dkrtdn|dkr2|dk r2td|dk rlgd}}x&|D]}||||f||7}qJW||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 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 either ``pathlib.Path`` or ``py._path.local.LocalPath``. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (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 -------- 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 Returns ------- boolean : Whether or not columns could become a MultiIndex css|]}t|tVqdS)N)rZr).0rr?r?r@ sz,_is_potential_multi_index..)rDrZr(all)rr?r?r@_is_potential_multi_indexsrcs"trfddt|DSS)z 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|]\}}|r|qSr?r?)rir8)r}r?r@ sz$_evaluate_usecols..)r enumerate)r}rIr?)r}r@_evaluate_usecolssrcs2fdd|D}t|dkr.tdj|d|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. 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Currently, any column that is involved with date parsing will not undergo such conversions. r)rNNcs:dk rt|r|}t|s*|}j|dS)N)rrrSZ set_noconvert)r)rIrr}r?r@_sets  z3CParserWrapper._set_noconvert_columns.._set) r rrr}sortrrIrZrQrrArn)rrXr=kr?)rIrr}r@rUs6              z%CParserWrapper._set_noconvert_columnscCs|jt|dS)N)rSset_error_bad_linesr;)rZstatusr?r?r@r[sz"CParserWrapper.set_error_bad_linesNc sy|j|}Wntk r|jrd|_||j}t||j|j|j dd\}}| |j |j dk r||ttfdd|}||fSYnXd|_|j}|jjr|jrtdg}xTt|jjD]D}|jdkr||}n||j|}|j||dd}||qWt|}|j dk rD||}||}t|}d d t||D}|||\}}nzt|}t|j}||}|j dk r||}d d |D} d d t||D}|||\}}||| |\}}| ||j }|||fS)NFrw)rwcs |dkS)Nrr?)item)rr?r@sz%CParserWrapper.read..z file structure not yet supportedT)r&cSsi|]\}\}}||qSr?r?)rrZrvr?r?r@ sz'CParserWrapper.read..cSsg|] }|dqS)rTr?)rrr?r?r@rsz'CParserWrapper.read..cSsi|]\}\}}||qSr?r?)rrZrr^r?r?r@r_s) rSr]rrr$r _get_empty_metarnr r`rVr%rr}_filter_usecolsrfilteritemsrIrVrrMr r_maybe_parse_datesrr*r/rrOrr,) rrUrbrIrrr7rrAr*r?)rr@r]s`                   zCParserWrapper.readcs>t|j|dk r:t|tkr:fddt|D}|S)Ncs$g|]\}}|ks|kr|qSr?r?)rrr8)r}r?r@r sz2CParserWrapper._filter_usecols..)rr}rDr)rrIr?)r}r@ras zCParserWrapper._filter_usecolscCsJt|jjd}d}|jjdkrB|jdk rBt||j|j\}}|_||fS)Nr)rrSrmrVrnrr )rrIZ idx_namesr?r?r@_get_index_names$s zCParserWrapper._get_index_namesTcCs|r||r||}|S)N)rr)rrArr&r?r?r@rd/s z!CParserWrapper._maybe_parse_dates)N)T) rrrrrr^rUr[r]rarerdr?r?r?r@r!s` 6 S rcOsd|d<t||S)a6 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 for the ordinary converter, 'high' for the high-precision converter, and 'round_trip' for the round-trip converter. rr)r\)argsr`r?r?r@ TextParser5s6rgcCstdd|DS)Ncss"|]}|dks|dkrdVqdS)rNrTr?)rr^r?r?r@rpsz#count_empty_vals..)rG)Zvalsr?r?r@count_empty_valsosrhc@seZdZddZddZddZd3dd Zd d Zd4d d ZddZ ddZ ddZ ddZ ddZ ddZddZddZddZd d!Zd"d#Zd$d%Zd&d'Zd(d)Zd*d+Zd,Zd-d.Zd/d0Zd5d1d2ZdS)6rc st|d_g_d_d_|d_|d_|d_|d_ t j r`j _ nfdd_ t |d _ |d _|d _tjtjrtj_|d _|d _|d_|d_|d_t|d\_}|d_|d_|d_|dpd_d_d|kr6|d_|d_ |d_!|d_"|d_#|d_$|d_%g_&t'rdnd}t(||jjjd \}}j)*|t+|d!rƈ,|n|_d_-.\_/_0_1t2j/d"kr&3j/j4j5\_/_4_5}t2j/_0n j/d_/t6j/_7j8st9j/\}_7_/d#_:j4dkrt|_4j;r<_=nd_=t2j$d"krt>d$j#dkrt?@d%j$_Ant?@d&j#j$f_AdS)'z Workhorse function for processing nested list into DataFrame Should be replaced by np.genfromtxt eventually? 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Ncss|]}t|tVqdS)N)rZr )rr r?r?r@rd sz/PythonParser._handle_usecols..rTz4If using multiple headers, usecols must be integers.cs"g|]}fddt|DqS)csg|]\}}|kr|qSr?r?)rrr) col_indicesr?r@ru sz;PythonParser._handle_usecols...)r)rr=)rr?r@ru sz0PythonParser._handle_usecols..) r}rranyrDr<rZr rrrrp)rrZ usecols_keyrr?)rr@r[ s&      zPythonParser._handle_usecolscCs$t|jdkr|jdS|SdS)zH Return a line from buffer, filling buffer if required. rN)rDrjr)rr?r?r@rz s zPythonParser._buffered_linecCs|s|St|dtjs|S|ds(|S|dd}tjrft|tsfy t|}Wntk rd|SX|tkrr|S|d}t|dkr|d|j krd}|d}|dd |d}|||}t||dkr|||dd7}|gSt|dkr|ddgSdgSdS)a- Checks whether the file begins with the BOM character. If it does, remove it. 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NrcCs||_d|_|rd|nd|_||_|dkr>|j||d|_n||_t|jttfsft dt |j xd|jD]Z}t|ttfrt |dkrt|dt tjt dfrt|dt tjt dfsnt d qnWdS) Nz z rO)rrpz=column specifications must be a list or tuple, input was a %rrrrTzEEach column specification must be 2 element tuple or list of integers)rbufferrfrydetect_colspecsrrZrrrrrrDr;rr)rrrrfryrprrr?r?r@r s"   zFixedWidthReader.__init__cCsf|dkrt}g}g}x@t|jD]2\}}||kr<||||t||kr"Pq"Wt||_|S)a 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. 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