pandas.read_fwf#

pandas.read_fwf(filepath_or_buffer, *, colspecs='infer', widths=None, infer_nrows=100, dtype_backend=_NoDefault.no_default, **kwds)[source]#

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_bufferstr, 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.

colspecslist 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’).

widthslist of int, optional

A list of field widths which can be used instead of ‘colspecs’ if the intervals are contiguous.

infer_nrowsint, default 100

The number of rows to consider when letting the parser determine the colspecs.

dtype_backend{“numpy_nullable”, “pyarrow”}, defaults to NumPy backed DataFrames

Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays, nullable dtypes are used for all dtypes that have a nullable implementation when “numpy_nullable” is set, pyarrow is used for all dtypes if “pyarrow” is set.

The dtype_backends are still experimential.

New in version 2.0.

**kwdsoptional

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.