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)[source]#

Read a table of fixed-width formatted lines into DataFrame.

Also supports optionally iterating or breaking of the fileinto chunks.

Additional help can be found in theonline docs for IO Tools.

Parameters:

filepath_or_bufferstr, path object, or file-like object

String, path object (implementingos.PathLike[str]), or file-likeobject implementing a textread() function.The string could be a URL.Valid URL schemes include http, ftp, s3, and file. For file URLs, a host isexpected. 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-widthfields of each line as half-open intervals (i.e., [from, to[ ).String value ‘infer’ can be used to instruct the parser to trydetecting the column specifications from the first 100 rows ofthe 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’ ifthe intervals are contiguous.

infer_nrowsint, default 100

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

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

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

• "numpy_nullable": returns nullable-dtype-backedDataFrame(default).

• "pyarrow": returns pyarrow-backed nullableArrowDtypeDataFrame.

Added in version 2.0.

**kwdsoptional

Optional keyword arguments can be passed toTextFileReader.

Returns:

DataFrame or TextFileReader

A comma-separated values (csv) file is returned as two-dimensionaldata 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')