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# pandas.read_fwf

pandas.read_fwf(filepath_or_buffer, *, colspecs='infer', widths=None, infer_nrows=100, dtype_backend=<no_default>, iterator=False, chunksize=None, **kwds)

  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.

    dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

      Back-end data type applied to the resultant `DataFrame` (still experimental). Behaviour is as follows:

      - `"numpy_nullable"`: returns nullable-dtype-backed `DataFrame` (default).

      - `"pyarrow"`: returns pyarrow-backed nullable `ArrowDtype` DataFrame.

      Added in version 2.0.

    **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')