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# pandas.io.parsers.read_fwf

`pandas.io.parsers.``read_fwf`(filepath_or_buffer, colspecs='infer', widths=None, **kwds)

  Read a table of fixed-width formatted lines into DataFrame

  Also supports optionally iterating or breaking of the file into chunks.

  Parameters : | filepath_or_buffer : string or file handle / StringIO. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.csv colspecs : list of pairs (int, int) or ‘infer’. optional A list of pairs (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 (default=’infer’). widths : list of ints. optional A list of field widths which can be used instead of ‘colspecs’ if the intervals are contiguous. lineterminator : string (length 1), default None Character to break file into lines. Only valid with C parser quotechar : string (length 1) 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 None Control field quoting behavior per `csv.QUOTE_*` constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). Default (None) results in QUOTE_MINIMAL behavior. skipinitialspace : boolean, default False Skip spaces after delimiter escapechar : string (length 1), default None One-character string used to escape delimiter when quoting is QUOTE_NONE. dtype : Type name or dict of column -> type Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} (Unsupported with engine=’python’) compression : {‘gzip’, ‘bz2’, None}, default None For on-the-fly decompression of on-disk data dialect : string or csv.Dialect instance, default None If None defaults to Excel dialect. Ignored if sep longer than 1 char See csv.Dialect documentation for more details header : int row number(s) to use as the column names, and the start of the data. Defaults to 0 if no `names` passed, otherwise `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 are skipped). Note that this parameter ignores commented lines, so header=0 denotes the first line of data rather than the first line of the file. skiprows : list-like or integer Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file index_col : int or sequence or False, default None 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) names : array-like List of column names to use. If file contains no header row, then you should explicitly pass header=None prefix : string or None (default) Prefix to add to column numbers when no header, e.g ‘X’ for X0, X1, ... na_values : list-like or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values true_values : list Values to consider as True false_values : list Values to consider as False keep_default_na : bool, default True If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to parse_dates : boolean, list of ints or names, list of lists, or dict If True -> try parsing the index. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’ A fast-path exists for iso8601-formatted dates. keep_date_col : boolean, default False If True and parse_dates specifies combining multiple columns then keep the original columns. date_parser : function 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. dayfirst : boolean, default False DD/MM format dates, international and European format thousands : str, default None Thousands separator comment : str, default None 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. Also, fully commented lines are ignored by the parameter header but not by skiprows. For example, if comment=’#’, parsing ‘#empty 1,2,3 a,b,c’ with `header=0` will result in ‘1,2,3’ being treated as the header. decimal : str, default ‘.’ Character to recognize as decimal point. E.g. use ‘,’ for European data nrows : int, default None Number of rows of file to read. Useful for reading pieces of large files iterator : boolean, default False Return TextFileReader object chunksize : int, default None Return TextFileReader object for iteration skipfooter : int, default 0 Number of lines at bottom of file to skip (Unsupported with engine=’c’) converters : dict. optional Dict of functions for converting values in certain columns. Keys can either be integers or column labels verbose : boolean, default False Indicate number of NA values placed in non-numeric columns delimiter : string, default None Alternative argument name for sep. Regular expressions are accepted. encoding : string, default None Encoding to use for UTF when reading/writing (ex. ‘utf-8’) squeeze : boolean, default False If the parsed data only contains one column then return a Series na_filter : boolean, 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 usecols : array-like Return a subset of the columns. Results in much faster parsing time and lower memory usage. mangle_dupe_cols : boolean, default True Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’ tupleize_cols : boolean, default False Leave a list of tuples on columns as is (default is to convert to a Multi Index on the columns) error_bad_lines : boolean, 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. (Only valid with C parser) warn_bad_lines : boolean, default True If error_bad_lines is False, and warn_bad_lines is True, a warning for each “bad line” will be output. (Only valid with C parser). infer_datetime_format : boolean, default False If True and parse_dates is enabled for a column, attempt to infer the datetime format to speed up the processing
  Returns : | result : DataFrame or TextParser Also, ‘delimiter’ is used to specify the filler character of the fields if it is not spaces (e.g., ‘~’).

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