Module « pandas »
Signature de la fonction read_fwf
def read_fwf(filepath_or_buffer: Union[ForwardRef('PathLike[str]'), str, IO[~T], io.RawIOBase, io.BufferedIOBase, io.TextIOBase, _io.TextIOWrapper, mmap.mmap], colspecs='infer', widths=None, infer_nrows=100, **kwds)
Description
read_fwf.__doc__
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
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
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.
Examples
--------
>>> pd.read_fwf('data.csv') # doctest: +SKIP
Améliorations / Corrections
Vous avez des améliorations (ou des corrections) à proposer pour ce document : je vous remerçie par avance de m'en faire part, cela m'aide à améliorer le site.
Emplacement :
Description des améliorations :